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1. Water fluxes pattern growth and identity in shoot meristems

Author

Juan Alonso-Serra, Ibrahim Cheddadi, Annamaria Kiss, Guillaume Cerutti, Marianne Lang, Sana Dieudonné, Claire Lionnet, Christophe Godin, and Olivier Hamant

Subjects
Science
Abstract

Abstract In multicellular organisms, tissue outgrowth creates a new water sink, modifying local hydraulic patterns. Although water fluxes are often considered passive by-products of development, their contribution to morphogenesis remains largely unexplored. Here, we mapped cell volumetric growth across the shoot apex in Arabidopsis thaliana. We found that, as organs grow, a subpopulation of cells at the organ-meristem boundary shrinks. Growth simulations using a model that integrates hydraulics and mechanics revealed water fluxes and predicted a water deficit for boundary cells. In planta, a water-soluble dye preferentially allocated to fast-growing tissues and failed to enter the boundary domain. Cell shrinkage next to fast-growing domains was also robust to different growth conditions and different topographies. Finally, a molecular signature of water deficit at the boundary confirmed our conclusion. Taken together, we propose that the differential sink strength of emerging organs prescribes the hydraulic patterns that define boundary domains at the shoot apex.

Published
2024
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2. A quantitative gibberellin signaling biosensor reveals a role for gibberellins in internode specification at the shoot apical meristem

Author

Bihai Shi, Amelia Felipo-Benavent, Guillaume Cerutti, Carlos Galvan-Ampudia, Lucas Jilli, Geraldine Brunoud, Jérome Mutterer, Elody Vallet, Lali Sakvarelidze-Achard, Jean-Michel Davière, Alejandro Navarro-Galiano, Ankit Walia, Shani Lazary, Jonathan Legrand, Roy Weinstain, Alexander M. Jones, Salomé Prat, Patrick Achard, and Teva Vernoux

Subjects
Science
Abstract

Abstract Growth at the shoot apical meristem (SAM) is essential for shoot architecture construction. The phytohormones gibberellins (GA) play a pivotal role in coordinating plant growth, but their role in the SAM remains mostly unknown. Here, we developed a ratiometric GA signaling biosensor by engineering one of the DELLA proteins, to suppress its master regulatory function in GA transcriptional responses while preserving its degradation upon GA sensing. We demonstrate that this degradation-based biosensor accurately reports on cellular changes in GA levels and perception during development. We used this biosensor to map GA signaling activity in the SAM. We show that high GA signaling is found primarily in cells located between organ primordia that are the precursors of internodes. By gain- and loss-of-function approaches, we further demonstrate that GAs regulate cell division plane orientation to establish the typical cellular organization of internodes, thus contributing to internode specification in the SAM.

Published
2024
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3. bHLH heterodimer complex variations regulate cell proliferation activity in the meristems of Arabidopsis thaliana

Author

Eliana Mor, Markéta Pernisová, Max Minne, Guillaume Cerutti, Dagmar Ripper, Jonah Nolf, Jennifer Andres, Laura Ragni, Matias D. Zurbriggen, Bert De Rybel, and Teva Vernoux

Subjects
Natural sciences, Biological sciences, Plant biology, Molecular plant pathology, Science
Abstract

Summary: Root, shoot, and lateral meristems are the main regions of cell proliferation in plants. It has been proposed that meristems might have evolved dedicated transcriptional networks to balance cell proliferation. Here, we show that basic helix-loop-helix (bHLH) transcription factor heterodimers formed by members of the TARGET OF MONOPTEROS5 (TMO5) and LONESOME HIGHWAY (LHW) subclades are general regulators of cell proliferation in all meristems. Yet, genetics and expression analyses suggest specific functions of these transcription factors in distinct meristems, possibly due to their expression domains determining heterodimer complex variations within meristems, and to a certain extent to the absence of some of them in a given meristem. Target gene specificity analysis for heterodimer complexes focusing on the LONELY GUY gene targets further suggests differences in transcriptional responses through heterodimer diversification that could allow a common bHLH heterodimer complex module to contribute to cell proliferation control in multiple meristems.

Published
2022
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4. Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets

Author

Anuradha Kar, Manuel Petit, Yassin Refahi, Guillaume Cerutti, Christophe Godin, and Jan Traas

Subjects
Biology (General), QH301-705.5
Abstract

Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state of the art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted, which isolates segmentation errors due to under- or oversegmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end-to-end 3D and were originally designed for cell boundary detection, show high performance and offer clear advantages in terms of adaptability to new data. Author summary In recent years, a number of deep learning (DL) algorithms based on computational neural networks have been developed, which claim to achieve high accuracy and automatic segmentation of three-dimensional (3D) microscopy images. Although these algorithms have received considerable attention in the literature, it is difficult to evaluate their relative performances, while it remains unclear whether they really perform better than other, more classical segmentation methods. To clarify these issues, we performed a detailed, quantitative analysis of a number of representative DL pipelines for cell instance segmentation from 3D confocal microscopy image datasets. We developed a protocol for benchmarking the performances of such DL-based segmentation pipelines using common training and test datasets, evaluation metrics, and visualizations. Using this protocol, we evaluated and compared 4 different DL pipelines to identify their strengths and limitations. A high performance non-DL method was also included in the evaluation. We show that DL pipelines may show significant differences in their performances depending on their model architecture and pipeline components but overall show excellent adaptability to unseen data. We also show that our benchmarking protocol could be extended to a variety of segmentation pipelines and datasets.

Published
2022

5. Temporal integration of auxin information for the regulation of patterning

Author

Carlos S Galvan-Ampudia, Guillaume Cerutti, Jonathan Legrand, Géraldine Brunoud, Raquel Martin-Arevalillo, Romain Azais, Vincent Bayle, Steven Moussu, Christian Wenzl, Yvon Jaillais, Jan U Lohmann, Christophe Godin, and Teva Vernoux

Subjects
shoot apical meristem, plant biology, Auxin, Medicine, Science, Biology (General), QH301-705.5
Abstract

Positional information is essential for coordinating the development of multicellular organisms. In plants, positional information provided by the hormone auxin regulates rhythmic organ production at the shoot apex, but the spatio-temporal dynamics of auxin gradients is unknown. We used quantitative imaging to demonstrate that auxin carries high-definition graded information not only in space but also in time. We show that, during organogenesis, temporal patterns of auxin arise from rhythmic centrifugal waves of high auxin travelling through the tissue faster than growth. We further demonstrate that temporal integration of auxin concentration is required to trigger the auxin-dependent transcription associated with organogenesis. This provides a mechanism to temporally differentiate sites of organ initiation and exemplifies how spatio-temporal positional information can be used to create rhythmicity.

Published
2020
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6. An Image Analysis Pipeline to Quantify Emerging Cracks in Materials or Adhesion Defects in Living Tissues

Author

Stéphane Verger, Guillaume Cerutti, and Olivier Hamant

Subjects
Biology (General), QH301-705.5
Abstract

Microcracks in materials reflect their mechanical properties. The quantification of the number or orientation of such cracks is thus essential in many fields, including engineering and geology. In biology, cracks in soft tissues can reflect adhesion defects, and the analysis of their pattern can help to deduce the magnitude and orientation of tensions in organs and tissues. Here, we describe a semi-automatic method amenable to analyze cell separations occurring in the epidermis of Arabidopsis thaliana seedlings. Our protocol is applicable to any image exhibiting small cracks, and thus also adapted to the analysis of emerging cracks in animal tissues and materials.

Published
2018
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7. Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets

Author

Guillaume Cerutti, Christophe Godin, Jan Traas, Yassin Refahi, Anuradha Kar, Petit M, Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Simulation et Analyse de la morphogenèse in siliCo (MOSAIC), Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), and ROSSI, Sabine

Subjects
Computer science, Confocal, media_common.quotation_subject, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Adaptability, Image (mathematics), Cellular and Molecular Neuroscience, Deep Learning, Imaging, Three-Dimensional, Market segmentation, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Genetics, Image Processing, Computer-Assisted, Segmentation, Molecular Biology, Ecology, Evolution, Behavior and Systematics, media_common, Ecology, business.industry, Deep learning, Pattern recognition, Image segmentation, Visualization, Benchmarking, Computational Theory and Mathematics, Modeling and Simulation, Artificial intelligence, business, Algorithms
Abstract

Segmenting three dimensional microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state-of-the-art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3 dimensional (3D) cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted which isolates segmentation errors due to under/over segmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end to end 3D and were originally designed for cell boundary detection, show high performance, and offer clear advantages in terms of adaptability to new data.Author summaryIn recent years a number of deep learning (DL) algorithms based on computational neural networks have been developed which claim to achieve high accuracy and automatic segmentation of 3D microscopy images. Although these algorithms have received considerable attention in the literature, it is difficult to evaluate their relative performances, while it remains unclear whether they really perform better than other, more classical segmentation methods.To clarify these issues, we performed a detailed, quantitative analysis of a number of representative DL pipelines for cell instance segmentation from 3D confocal microscopy image datasets. We developed a protocol for benchmarking the performances of such DL based segmentation pipelines using common training and test datasets, evaluation metrics and visualizations. Using this protocol, we evaluated and compared four different DL pipelines to identify their strengths and limitations. A high performance non-DL method was also included in the evaluation. We show that DL pipelines may show significant differences in their performances depending on their model architecture and pipeline components but overall show excellent adaptability to unseen data. We also show that our benchmarking protocol could be extended to a variety of segmentation pipelines and datasets.

Published
2022

8. A quantitative gibberellin signalling biosensor reveals a role for gibberellins in internode specification at the shoot apical meristem

Author

Bihai Shi, Amelia Felipo-Benavent, Guillaume Cerutti, Carlos Galvan-Ampudia, Lucas Jilli, Geraldine Brunoud, Jérome Mutterer, Elody Vallet, Lali Sakvarelidze-Achard, Jean-Michel Davière, Alejandro Navarro-Galiano, Ankit Walia, Shani Lazary, Jonathan Legrand, Roy Weinstain, Alexander M. Jones, Salomé Prat, Patrick Achard, Teva Vernoux, Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de biologie moléculaire des plantes (IBMP), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Simulation et Analyse de la morphogenèse in siliCo (MOSAIC), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), and École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
0106 biological sciences, 0303 health sciences, Chemistry, Repressor, food and beverages, Meristem, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, 01 natural sciences, Cell biology, 03 medical and health sciences, Signalling, Shoot, Gibberellin, Primordium, Biosensor, Function (biology), 030304 developmental biology, 010606 plant biology & botany
Abstract

Growth at the shoot apical meristem (SAM) is essential for shoot architecture construction. The phytohormones gibberellins (GA) play a pivotal role in coordinating plant growth, but their role in the SAM remains mostly unknown. Here, we developed a ratiometric GA signalling biosensor by engineering one of the DELLA proteins, to suppress its master regulatory function in GA transcriptional responses while preserving its degradation upon GA sensing. We demonstrate that this novel degradation-based biosensor accurately reports on cellular changes in GA levels and perception during development. We used this biosensor to map GA signalling activity in the SAM. We show that high GA signalling is found primarily in cells located between organ primordia that are the precursors of internodes. By gain- and loss-of-function approaches, we further demonstrate that GAs regulate cell division plane orientation to establish the typical cellular organisation of internodes, thus contributing to internode specification in the SAM.

Published
2021

9. bHLH heterodimer complex variations regulate cell proliferation activity in the meristems of

Author

Eliana, Mor, Markéta, Pernisová, Max, Minne, Guillaume, Cerutti, Dagmar, Ripper, Jonah, Nolf, Jennifer, Andres, Laura, Ragni, Matias D, Zurbriggen, Bert, De Rybel, and Teva, Vernoux

Abstract

Root, shoot, and lateral meristems are the main regions of cell proliferation in plants. It has been proposed that meristems might have evolved dedicated transcriptional networks to balance cell proliferation. Here, we show that basic helix-loop-helix (bHLH) transcription factor heterodimers formed by members of the TARGET OF MONOPTEROS5 (TMO5) and LONESOME HIGHWAY (LHW) subclades are general regulators of cell proliferation in all meristems. Yet, genetics and expression analyses suggest specific functions of these transcription factors in distinct meristems, possibly due to their expression domains determining heterodimer complex variations within meristems, and to a certain extent to the absence of some of them in a given meristem. Target gene specificity analysis for heterodimer complexes focusing on the

Published
2021

11. Temporal integration of auxin information for the regulation of patterning

Author

Carlos S. Galvan-Ampudia, Yvon Jaillais, Christian Wenzl, Guillaume Cerutti, Jonathan Legrand, Steven Moussu, Christophe Godin, Vincent Bayle, Raquel Martin-Arevalillo, Romain Azaïs, Géraldine Brunoud, Jan U. Lohmann, Teva Vernoux, Reproduction et développement des plantes (RDP), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Simulation et Analyse de la morphogenèse in siliCo (MOSAIC), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Heidelberg University, Centre for Organismal Studies, Universität Heidelberg [Heidelberg], HFSP RPG0054-2013, ANR-12-BSV6-0005,AuxiFlo,Couplage entre le réseau transcriptionnel de l'auxine et l'identité florale(2012), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), and Universität Heidelberg [Heidelberg] = Heidelberg University

Subjects
0106 biological sciences, 0301 basic medicine, Time Factors, Transcription, Genetic, Arabidopsis, Plant Biology, Biosensing Techniques, 01 natural sciences, Organogenesis, Plant, Plant Growth Regulators, Gene Expression Regulation, Plant, Genes, Reporter, heterocyclic compounds, Auxin, Biology (General), [SDV.BDD]Life Sciences [q-bio]/Development Biology, chemistry.chemical_classification, shoot apical meristem, Microscopy, Confocal, General Neuroscience, food and beverages, General Medicine, Plants, Genetically Modified, Cell biology, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Medicine, Research Article, Quantitative imaging, QH301-705.5, Science, Organogenesis, Biology, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, General Immunology and Microbiology, Indoleacetic Acids, fungi, Meristem, Plant biology, Shoot apex, Multicellular organism, 030104 developmental biology, chemistry, A. thaliana, Developmental biology, 010606 plant biology & botany, Developmental Biology
Abstract

Positional information is essential for coordinating the development of multicellular organisms. In plants, positional information provided by the hormone auxin regulates rhythmic organ production at the shoot apex, but the spatio-temporal dynamics of auxin gradients is unknown. We used quantitative imaging to demonstrate that auxin carries high-definition graded information not only in space but also in time. We show that, during organogenesis, temporal patterns of auxin arise from rhythmic centrifugal waves of high auxin travelling through the tissue faster than growth. We further demonstrate that temporal integration of auxin concentration is required to trigger the auxin-dependent transcription associated with organogenesis. This provides a mechanism to temporally differentiate sites of organ initiation and exemplifies how spatio-temporal positional information can be used to create rhythmicity., eLife digest Plants, like animals and many other multicellular organisms, control their body architecture by creating organized patterns of cells. These patterns are generally defined by signal molecules whose levels differ across the tissue and change over time. This tells the cells where they are located in the tissue and therefore helps them know what tasks to perform. A plant hormone called auxin is one such signal molecule and it controls when and where plants produce new leaves and flowers. Over time, this process gives rise to the dashing arrangements of spiraling organs exhibited by many plant species. The leaves and flowers form from a relatively small group of cells at the tip of a growing stem known as the shoot apical meristem. Auxin accumulates at precise locations within the shoot apical meristem before cells activate the genes required to make a new leaf or flower. However, the precise role of auxin in forming these new organs remained unclear because the tools to observe the process in enough detail were lacking. Galvan-Ampudia, Cerutti et al. have now developed new microscopy and computational approaches to observe auxin in a small plant known as Arabidopsis thaliana. This showed that dozens of shoot apical meristems exhibited very similar patterns of auxin. Images taken over a period of several hours showed that the locations where auxin accumulated were not fixed on a group of cells but instead shifted away from the center of the shoot apical meristems faster than the tissue grew. This suggested the cells experience rapidly changing levels of auxin. Further experiments revealed that the cells needed to be exposed to a high level of auxin over time to activate genes required to form an organ. This mechanism sheds a new light on how auxin regulates when and where plants make new leaves and flowers. The tools developed by Galvan-Ampudia, Cerutti et al. could be used to study the role of auxin in other plant tissues, and to investigate how plants regulate the response to other plant hormones.

Published
2019

12. Treex : a Python package for manipulating rooted trees

Author

Didier Gemmerle, Romain Azaïs, Florian Ingels, Guillaume Cerutti, Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Simulation et Analyse de la morphogenèse in siliCo (MOSAIC), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut Élie Cartan de Lorraine (IECL), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), The work of Romain Azaïs and Florian Ingles has been partially supported by the European Union’s H2020 project ROMI, and École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
0303 health sciences, Source code, Theoretical computer science, Java, computer.internet_protocol, Computer science, media_common.quotation_subject, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Python (programming language), Data structure, 01 natural sciences, Vertex (geometry), 03 medical and health sciences, 0103 physical sciences, Edit distance, Game tree, 010303 astronomy & astrophysics, computer, XML, 030304 developmental biology, media_common, computer.programming_language
Abstract

International audience; Trees form an expanded family of combinatorial objects that offers a wide range of application fields, from plant modeling to XML files analysis through study of lineage trees. One can also mention classification trees that arise in statistical learning, as well as game trees. In all these examples, the underlying structure is a rooted tree which can be ordered (if the order of siblings is significant) or not. For the sake of modeling, the vertices of a tree might be augmented with labels describing their properties: for example, in a cell lineage tree, each vertex represents a cell and the labels might describe its volume, the tissue type, etc. Despite the diversity of applications, one may want a unique data structure to encode rooted trees and perform operations on them.Treex is a Python package for manipulating rooted trees, ordered or not, with or without labels on their vertices. Basically, Treex allows (i) random generation of trees, (ii) edit operations (e.g., add or remove vertices or labels), (iii) visualization of structures and their properties (in command line or a Matplotlib figure and exportation to TeX), (iv) conversion to different formats, and (v) application of various algorithms. Concerning the latter two, coding processes (Pitman, 2006) have been implemented, as well as DAG compression (Godin & Ferraro, 2010). In addition, comparison between trees can be performed via an edit distance algorithm (Azais, Durand, & Godin, 2019). Self-nested approximations of trees (Godin & Ferraro, 2010, Azais (2017), Azais et al. (2019)) have been implemented through different algorithms. To the best of our knowledge, treex is the only Python library that permits encoding of rooted trees in various formats together with such a diversity of treatments. Let us mention the related Java library TED (Pawlik & Augsten, 2016) for efficient edit distance algorithms. Treex offers converters to the standard encoding of nested brackets (see for instance (Aho, Hopcroft, & Ullman, 1974)) and L-strings as manipulated by L-Py, a simulation framework for modeling plant architectures (Boudon, Pradal, Cokelaer, Prusinkiewicz, & Godin, 2012). Numerical experiments and/or figures of recent publications (Azais, Genadot, & Henry, 2019, Azais (2017), Azais et al. (2019)) have been made using the current or previous versions of treex. Furthermore, ongoing academic projects on the development and implementation of supervised classification methods for tree data, the study of lineage trees, as well as investigations on plant modeling, make intensive use of structures and algorithms implemented in treex. Treex is open source and distributed under the LGPL License. The source code is hosted on GitLab. Releases are automatically built and tested for 64-bit Linux and Mac OS X machines using Jenkins CI, and packaged on Anaconda Cloud (Azais, Cerutti, Gemmerle, & Ingels, 2019a). The documentation of all classes, methods and functions is built upon release and made available online (Azais, Cerutti, Gemmerle, & Ingels, 2019b).

Published
2019

13. From spatio-temporal morphogenetic gradients to rhythmic patterning at the shoot apex

Author

Teva Vernoux, Carlos S. Galvan-Ampudia, Christian Wenzl, Guillaume Cerutti, Jan U. Lohmann, Steven Moussu, Jonathan Legrand, Christophe Godin, Romain Azaïs, Géraldine Brunoud, Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Simulation et Analyse de la morphogenèse in siliCo (MOSAIC), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Heidelberg University, École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Lyon (ENS Lyon)

Subjects
chemistry.chemical_classification, 0303 health sciences, biology, fungi, Regulator, food and beverages, Organogenesis, Meristem, Cell biology, 03 medical and health sciences, 0302 clinical medicine, Rhythm, Histone, [SDV.BDD.EO]Life Sciences [q-bio]/Development Biology/Embryology and Organogenesis, chemistry, Auxin, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], biology.protein, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, heterocyclic compounds, Developmental biology, 030217 neurology & neurosurgery, 030304 developmental biology, Morphogen
Abstract

A key question in developmental biology is how morphogenetic regulators control patterning. Recent findings have raised an important question: do morphogenetic signals carry information not only in space, as originally proposed in the morphogen concept, but also in time? The hormone auxin is an essential plant morphogenetic regulator that drives rhythmic organogenesis at the shoot apical meristem. Here, we used a quantitative imaging approach to map auxin distribution and response. We demonstrate the existence of high-definition spatio-temporal auxin distribution in the meristem. We provide evidence that developing organs are auxin-emitting centers that could allow self-sustained distribution of auxin through a structured auxin transport network converging on the meristem center. We finally demonstrate that regulation of histone acetylation allows cells to measure the duration of the exposition to auxin preceding organ initiation, providing a remarkable example of how both spatial and temporal morphogenetic information generates rhythmic patterning.

Published
2018

14. Bark and Leaf Fusion Systems to Improve Automatic Tree Species Recognition

Author

Lionel Valet, Didier Coquin, Guillaume Cerutti, Sarah Bertrand, Rihab Ben Ameur, Laure Tougne, Extraction de Caractéristiques et Identification ( imagine ), Laboratoire d'InfoRmatique en Image et Systèmes d'information ( LIRIS ), Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ) -Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ), Modeling plant morphogenesis at different scales, from genes to phenotype ( VIRTUAL PLANTS ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de la Recherche Agronomique ( INRA ) -Centre de coopération internationale en recherche agronomique pour le développement [CIRAD] : UMR51, ANR-15-CE38-0004,ReVeRIES,Reconnaissance de Végétaux Récréative, Interactive et Educative sur Smartphone, Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Simulation et Analyse de la morphogenèse in siliCo (MOSAIC), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Reproduction et développement des plantes (RDP), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), ANR-15-CE38-0004,ReVeRIES,Reconnaissance de Végétaux Récréative, Interactive et Educative sur Smartphone(2015), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Lyon (ENS Lyon)

Subjects
[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR], [ INFO ] Computer Science [cs], Computer science, 02 engineering and technology, 01 natural sciences, Bark, Tree recognition, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Fusion, Ecology, Evolution, Behavior and Systematics, Ecology, business.industry, Applied Mathematics, Ecological Modeling, 010401 analytical chemistry, Confusion matrix, Pattern recognition, Distinctive feature, 15. Life on land, 0104 chemical sciences, Computer Science Applications, Tree (data structure), Leaf, Computational Theory and Mathematics, Modeling and Simulation, visual_art, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Combination, visual_art.visual_art_medium, 020201 artificial intelligence & image processing, Identification (biology), Artificial intelligence, business, Tree species
Abstract

International audience; For everyone, the identification of tree is a difficult task. The main organ of the plant used generally to identify a tree is the leaf. However, due to the large variability of the shapes of leaves, it is difficult to obtain good recognition results. Moreover, sometimes the bark is a very distinctive feature and we think it may be possible to improve the recognition rate by considering it. The main purpose of this article is to investigate how we can combine the features extracted respectively from the leaf and the bark images to recognize the tree the photos come from. An important point is the consideration of the confusion matrix that can be constructed between several species, when the form of a leaf or the shape of a bark is common to a number of tree species. So, we present various strategies of fusion including belief functions and compare them on a public database of 72 species of trees and shrubs, which can be find in metropolitan France.

Published
2018

15. Bark Recognition to Improve Leaf-based Classification in Didactic Tree Species Identification

Author

Sarah Bertrand, Guillaume Cerutti, Laure Tougne, Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-15-CE38-0004,ReVeRIES,Reconnaissance de Végétaux Récréative, Interactive et Educative sur Smartphone, Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ANR-15-CE38-0004,ReVeRIES,Reconnaissance de Végétaux Récréative, Interactive et Educative sur Smartphone(2015), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
0106 biological sciences, Smart phone, Computer science, Feature vector, 02 engineering and technology, 010603 evolutionary biology, 01 natural sciences, Bark, Tree recognition, 0202 electrical engineering, electronic engineering, information engineering, Recognition system, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Pattern recognition, 15. Life on land, Smart-phone, Leaf, Identification (information), Tree (data structure), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], visual_art, visual_art.visual_art_medium, Artificial intelligence, business, Tree species
Abstract

International audience; In this paper, we propose a botanical approach for tree species classification through automatic bark analysis. The proposed method is based on specific descriptors inspired by the characterization keys used by botanists, from visual bark texture criteria. The descriptors and the recognition system are developed in order to run on a mobile device, without any network access. Our obtained results show a similar rate when compared to the state of the art in tree species identification from bark images with a small feature vector. Furthermore, we also demonstrate that the consideration of the bark identification significantly improves the performance of tree classification based on leaf only

Published
2017

16. 3D Tessellation of Plant Tissue A dual optimization approach to cell-level meristem reconstruction from microscopy images

Author

Sophie Ribes, Carlos S. Galvan-Ampudia, Teva Vernoux, Guillaume Cerutti, Christophe Godin, Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Lyon (ENS Lyon)

Subjects
shoot apical meristem, Tessellation (computer graphics), business.industry, mesh optimization, voronoi diagrams, Image segmentation, Iterative reconstruction, Energy minimization, confocal microscopy, Visualization, Simplicial complex, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Microscopy, topo-logical transformation, Polygon mesh, Computer vision, Artificial intelligence, business, Biological system, Mathematics
Abstract

International audience; The goal of this paper is the reconstruction of topo-logically accurate 3-dimensional triangular meshes representing a complex, multi-layered plant tissue structure. Based on time sequences of meristem images of the model plant Arabidopsis thaliana, displaying fluorescence markers on either cell membranes or cell nuclei under confocal laser scanning microscopy, we aim at obtaining faithful reconstructions of all the cell walls in the tissue. In the presented method, the problem is tackled under the angle of topology, and the shape of the cells is seen as the dual geometry of a 3-d simplicial complex accounting for their adjacency relationships. We present a method for optimizing such complexes using an energy minimization process, designed to make them fit to the actual adjacencies in the tissue. The resulting dual meshes constitute a light discrete representation of the cell surfaces that enables fast visualization, and quantitative analysis, and allows in silico physical and mechanical simulations on real-world data.

Published
2015

17. Meshing Meristems - An Iterative Mesh Optimization Method for Modeling Plant Tissue at Cell Resolution

Author

Christophe Godin, Guillaume Cerutti, Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), HFSP, Cerutti, Guillaume, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
0106 biological sciences, Surface (mathematics), Deformable Models, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Tissue reconstruction, Biology, 01 natural sciences, Image (mathematics), 03 medical and health sciences, Botany, Morphogenesis, Polygon mesh, Representation (mathematics), 030304 developmental biology, Energy functional, ComputingMethodologies_COMPUTERGRAPHICS, 0303 health sciences, Meristem, Visualization, Shoot Apical Meristem, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Cell Reconstruction, Mesh Optimization, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Biological system, 010606 plant biology & botany
Abstract

We address in this paper the problem of reconstructing a mesh representation of plant cells in a complex, multi-layered tissue structure, based on segmented images obtained from confocal microscopy of shoot apical meristem of model plant Arabidopsis thaliana. The construction of such mesh structures for plant tissues is currently a missing step in the existing image analysis pipelines. We propose a method for optimizing the surface triangular meshes representing the tissue simultaneously along several criteria, based on an initial low-quality mesh. The mesh geometry is deformed by iteratively minimizing an energy functional defined over this discrete surface representation. This optimization results in a light discrete representation of the cell surfaces that enables fast visualization, and quantitative analysis, and gives way to in silico physical and mechanical simulations on real-world data. We provide a framework for evaluating the quality of the cell tissue reconstruction, that underlines the ability of our method to fit multiple optimization criteria.

Published
2015

18. Tree leaves extraction in natural images: Comparative study of pre-processing tools and segmentation methods

Author

Jonathan Weber, Laure Tougne, Guillaume Cerutti, Camille Kurtz, Manuel Grand-Brochier, Antoine Vacavant, Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Informatique Paris Descartes (LIPADE - EA 2517), Université Paris Descartes - Paris 5 (UPD5), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Image Science for Interventional Techniques ( ISIT ), Université d'Auvergne - Clermont-Ferrand I ( UdA ) -Clermont Université-Centre National de la Recherche Scientifique ( CNRS ), Modeling plant morphogenesis at different scales, from genes to phenotype ( VIRTUAL PLANTS ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de la Recherche Agronomique ( INRA ) -Centre de coopération internationale en recherche agronomique pour le développement [CIRAD] : UMR51, Laboratoire d'Informatique Paris Descartes ( LIPADE - EA 2517 ), Université Paris Descartes - Paris 5 ( UPD5 ), Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Extraction de Caractéristiques et Identification ( imagine ), Laboratoire d'InfoRmatique en Image et Systèmes d'information ( LIRIS ), Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ) -Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ), Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)

Subjects
Computer science, Tree Leaves Segmentation, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Sensitivity and Specificity, Pattern Recognition, Automated, Trees, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, Image Interpretation, Computer-Assisted, Photography, Preprocessor, Segmentation, Comparative Study, Cluster analysis, Active contour model, business.industry, Distance Map, Guided Active Contour, Reproducibility of Results, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Image segmentation, 15. Life on land, Image Enhancement, Pre-Processing Tools, Computer Graphics and Computer-Aided Design, Random forest, Plant Leaves, Tree (data structure), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Pattern recognition (psychology), Artificial intelligence, business, Distance transform, Algorithms, Software, Environmental Monitoring
Abstract

International audience; In this paper, we propose a comparative study of various segmentation methods applied to the extraction of tree leaves from natural images. This study follows the design of a mobile application, developed by Cerutti et al. (published in ReVeS Participation-Tree Species Classification Using Random Forests and Botanical Features. CLEF 2012), to highlight the impact of the choices made for segmentation aspects. All the tests are based on a database of 232 images of tree leaves depicted on natural background from smartphones acquisitions. We also propose to study the improvements, in terms of performance, by using pre-processing tools such as the interaction between the user and the application through an input stroke, as well as the use of color distance maps. The results presented in this paper shows that the method developed by Cerutti et al. (denoted Guided Active Contour), obtains the best score for almost all observation criteria. Finally we detail our online benchmark composed of 14 unsupervised methods and 6 supervised ones.

Published
2015

19. Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification

Author

Guillaume Cerutti, Julien Mille, Laure Tougne, Didier Coquin, Antoine Vacavant, Geometry Processing and Constrained Optimization (M2DisCo), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Extraction de Caractéristiques et Identification (imagine), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
geometric features, Process (engineering), Semantic interpretation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-space segmentation, 02 engineering and technology, Image (mathematics), plant recognition, 0202 electrical engineering, electronic engineering, information engineering, tree leaf, Segmentation, image segmentation, Mathematics, Active contour model, business.industry, Pattern recognition, 04 agricultural and veterinary sciences, Image segmentation, 15. Life on land, active contours, Identification (information), classification, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Signal Processing, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software, natural background
Abstract

International audience; With the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia.

Published
2013

20. Curvature-Scale-based Contour Understanding for Leaf Margin Shape Recognition and Species Identification

Author

Guillaume Cerutti, Laure Tougne, Didier Coquin, Antoine Vacavant, Cerutti, Guillaume, Geometry Processing and Constrained Optimization (M2DisCo), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Extraction de Caractéristiques et Identification (imagine), Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)

Subjects
leaf identification, contour characterization, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, shape classification, curvature-scale space, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

International audience; In the frame of a tree species identifying mobile application, designed for a wide scope of users, and with didactic purposes, we developed a method based on the computation of explicit leaf shape descriptors inspired by the criteria used in botany. This paper focuses on the characterization of the leaf contour, the extraction of its properties, and its description using botanical terms. Contour properties are investigated using the Curvature-Scale Space representation, the potential teeth explicitly extracted and described, and the margin classified into a set of inferred shape classes. Results are presented for both margin shape characterization, and leaf classification over nearly 80 tree species.

Published
2013

21. ReVeS Participation - Tree Species Classification using Random Forests and Botanical Features

Author

Guillaume Cerutti, Violaine Antoine, Laure Tougne, Julien Mille, Lionel Valet, Didier Coquin, Antoine Vacavant, Geometry Processing and Constrained Optimization (M2DisCo), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Extraction de Caractéristiques et Identification (imagine), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Cerutti, Guillaume, Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Abstract

International audience; This paper summarizes the participation of the ReVeS project to the ImageCLEF 2012 Plant Identification task. Aiming to develop a system for tree leaf identification on mobile devices, our method is designed to cope with the challenges of complex natural images and to enable a didactic interaction with the user. The approach relies on a two step model-driven segmentation and on the evaluation of high-level characteristics that make a semantic interpretation possible, as well as more generic shape features. All these descriptors are combined in a random forest classification algorithm, and their significance evaluated. Our team ranks 4th overall, 3rd on natural images, which constitutes a very satisfying performance with respect to the project's objectives.

Published
2012

22. A Parametric Active Polygon for Leaf Segmentation and Shape Estimation

Author

Guillaume Cerutti, Didier Coquin, Laure Tougne, Antoine Vacavant, Geometry Processing and Constrained Optimization (M2DisCo), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Cerutti, Guillaume, Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Active contour model, Basis (linear algebra), business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Process (computing), Scale-space segmentation, 020207 software engineering, 02 engineering and technology, 15. Life on land, Leaf identification, Tree (data structure), Segmentation, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Polygon, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, ComputingMethodologies_COMPUTERGRAPHICS, Parametric statistics
Abstract

International audience; The identication of tree leaves from photographs taken in a natural environment is a di cult task where the quality of the seg- mentation is of primary importance. In this paper we present a system that combines a rst, unre ned segmentation step, with an estimation of descriptors depicting the general shape of a simple leaf. It is based on a light polygonal model, built to represent most of the leaf shapes, that will be deformed to t the leaf in the image. Avoiding some classic obstacles of active contour models, this approach gives promising results, even on complex natural photographs, and constitutes a solid basis for a leaf recognition process.

Published
2011

23. Guiding Active Contours for Tree Leaf Segmentation and Identification

Author

Guillaume Cerutti, Laure Tougne, Julien Mille, Antoine Vacavant, Didier Coquin, Cerutti, Guillaume, Geometry Processing and Constrained Optimization (M2DisCo), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Extraction de Caractéristiques et Identification (imagine), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université, Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), and Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS)

Subjects
Segmentation, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Leaf identification
Abstract

International audience; In the process of tree identi cation from pictures of leaves in a natural background, retrieving an accurate contour is a challenging and crucial issue. In this paper we introduce a method designed to deal with the obstacles raised by such complex images, for simple and lobed tree leaves. A rst segmentation step based on a light polygonal leaf model is first performed, and later used to guide the evolution of an active contour. Combining global shape descriptors given by the polygonal model with local curvature-based features, the leaves are then classi ed over nearly 50 tree species.

Published
2011

24. Current achievements of the DELPHI ring imaging Cherenkov detector

Author

C. Aubret, G. Polok, E. C. Katsoufis, D. Poutot, J. M. Brunet, E. Christophel, Jerzy Michalowski, G. E. Theodosiou, L. Traspedini, H. Rahmani, G. W. Van Apeldoorn, Mario Sannino, D. Fassouliotis, L. K. Resvanis, J. P. Engel, S. Maltezos, Christine Kourkoumelis, E. Dahl-Jensen, M. Chevry, G. Damgaard, G. Mourgue, Th. D. Papadopoulou, M. Davenport, G. Tripodi, E. Albrecht, S Jaaskelainen, O. Ullaland, A. Lopez Agüera, P. Juillot, V. Gracco, S. Ilie, Paul Baillon, Pavlos Ioannou, Kathy Huet, J. Dolbeau, P. Cavalli, B. Dalmagne, F. Delbecque, J. Thadome, Tord Ekelof, A. Fontenille, Daniel Bloch, Borge Svane Nielsen, E. Zevgolatakos, D. Z. Toet, P. Stassi, Wolfgang Adam, S. Haider, D. Gillespie, G. Sajot, Peter Kluit, M. Battaglia, Jorge-Armando Benitez Garcia, E. Schyns, G. Lecoeur, Demetrios Loukas, S. Katsanevas, M. Reale, P. Carrie, Guillaume Cerutti, Manolis Dris, T. A. Filippas, Evangelos Gazis, C. Joram, Olga Botner, F. Hahn, F. Montano, E. Karvelas, M. Dracos, N. Dimitriou, B. Koene, M. J. Tobar, D. Allen, N. J. Kjaer, S. Squarcia, D. Fraissard, Claire Bourdarios, G. Tristram, Andromachi Tsirou, L.-E. Lindqvist, Andre Augustinus, A. Buys, Allan Hallgren, H. Furstenau, G. Lenzen, R. Strub, E. Fokitis, W. Hao, R. Nicolaidou, M. L. Andrieux, J. Medbo, F. Fontanelli, A. Markou, A. Petrolini, L. Guglielmi, LEP (IHEF, IoP, FNWI), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches Subatomiques (IReS), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Cancéropôle du Grand Est-Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS), Physique Corpusculaire et Cosmologie - Collège de France (PCC), Collège de France (CdF (institution))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'Accélérateur Linéaire (LAL), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Collège de France (CdF)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), and Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Physics, Nuclear and High Energy Physics, Physics::Instrumentation and Detectors, Cherenkov detector, Detector, Solid angle, Signal, Ring-imaging Cherenkov detector, law.invention, Nuclear physics, law, Carbon–fluorine bond, Calibration, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], High Energy Physics::Experiment, Angular resolution, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Instrumentation
Abstract

The DELPHI experiment has already collected 2.5 million Z 0 decays with the ring imaging Cherenkov detector (RICH) operational. This detector, covering most of the solid angle, is designed to perform π K separation from 0.8 to 20 GeV/ c and K/p separation from 0.8 to 35 GeV/ c . After a brief detector description we discuss the actual operating conditions, the data monitoring and the signal treatment. The collected data from Z 0 decays and the detector response to signals from the calibration system are used to evaluate the performance of the RICH system.

Published
1996

25. Performance of the ring imaging Cherenkov detector of DELPHI

Author

G. W. Van Apeldoorn, D. Poutot, Y. Arnoud, E. Dahl-Jensen, A. Buys, D. Fraissard, J. P. Engel, L. Guglielmi, G. Polok, Allan Hallgren, L. Traspedini, L. K. Resvanis, Evangelos Gazis, P. Juillot, Th. D. Papadopoulou, B. Koene, G. Damgaard, M. Dracos, M. Chevry, A. Lopez Agüera, J. M. Brunet, V. Gracco, Pavlos Ioannou, G. Lecoeur, S. Ilie, F. Delbecque, Mario Sannino, F. Hahn, W. Hao, J. Thadome, D. Allen, G. Mourgue, Guillaume Cerutti, Olga Botner, H. Rahmani, M. Davenport, Kathy Huet, D. Z. Toet, G. Tripodi, D. Fassouliotis, Claire Bourdarios, G. E. Theodosiou, Borge Svane Nielsen, J. Dolbeau, N. J. Kjaer, O. Ullaland, Jorge-Armando Benitez Garcia, Manolis Dris, E. Albrecht, G. Tristram, Peter Kluit, Daniel Bloch, E. C. Katsoufis, Wolfgang Adam, Tord Ekelof, Andromachi Tsirou, Andre Augustinus, Paul Baillon, S. Katsanevas, T. A. Filippas, S. Haider, Jerzy Michalowski, R. Nicolaidou, P. Stassi, F. Fontanelli, E. Christophel, S. Maltezos, A. Fontenille, M. J. Tobar, P. Cavalli, B. Dalmagne, G. Lenzen, R. Strub, F. Montano, E. Karvelas, N. Dimitriou, L.-E. Lindqvist, S. Squarcia, D. Gillespie, E. Schyns, M. Reale, E. Fokitis, Demetrios Loukas, S Jaaskelainen, C. Kourkoumelis, H. Furstenau, E. Zevgolatakos, M. Battaglia, P. Carrie, M. L. Andrieux, J. Medbo, A. Markou, A. Petrolini, C. Aubret, G. Sajot, C. Joram, Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), Physique Corpusculaire et Cosmologie - Collège de France (PCC), Collège de France (CdF (institution))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches Subatomiques (IReS), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Cancéropôle du Grand Est-Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), and Collège de France (CdF)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Physics, Nuclear and High Energy Physics, Large Hadron Collider, Physics::Instrumentation and Detectors, Cherenkov detector, Detector, Ring-imaging Cherenkov detector, Particle detector, law.invention, Nuclear physics, law, Large Electron–Positron Collider, Hermetic detector, High Energy Physics::Experiment, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Nuclear Experiment, Instrumentation, Cherenkov radiation
Abstract

The general purpose particle detector DELPHI at the Large Electron Positron collider at CERN was built to give the complete information of each event. DELPHI uses ring imaging Cherenkov counters to provide hadron identification in most of the momentum range below 45 GeV/ c and over almost the full solid angle. Charged particles traversing gaseous and liquid fluorocarbon radiators create photons used for Cherenkov angle reconstruction. Some of the design features of the detector will be presented, with emphasis on the experience which was gained in the operation of these large systems. The hadron identification power of the ring imaging Cherenkov detector closely meets the main design values. Data processing and performance of the detector will be discussed using dimuon events collected during 1994. Pion rejection factors for kaon tagging will be shown.

Published
1995

26. RECENT RESULTS OF THE FORWARD RING IMAGING CHERENKOV DETECTOR OF THE DELPHI EXPERIMENT AT LEP

Author

Jorge-Armando Benitez Garcia, G. Damgaard, V. Gracco, A. Fontenille, G. Tripodi, A.S. de la Vega, L.-E. Lindqvist, Guillaume Cerutti, A. Petrolini, P. Cavalli, T. Henkes, A. Lopez Aguera, G. Sajot, J. Thadome, Mario Sannino, F. Montano, P. Carecchio, A. Maltezos, A. Budziak, E. Schyns, E. Karvelas, Wolfgang Adam, B. Koene, N. Dimitriou, J.M. Ostler, E. Albrecht, Tord Ekelof, W. Hao, Allan Hallgren, L. Traspedini, Andre Augustinus, Demetrios Loukas, Olga Botner, G. Polok, G. Lenzen, G. E. Theodosiou, O. Ullaland, A. Markou, E. Dahl-Jensen, L. O. Eek, F. Fontanelli, S. Squarcia, A. Florek, M. Zavrtanik, Borge Svane Nielsen, B. Florek, Jerzy Michalowski, and J. Medbo

Subjects
Physics, Nuclear and High Energy Physics, Physics::Instrumentation and Detectors, Computer Science::Software Engineering, Particle accelerator, Particle identification, Particle detector, Ring-imaging Cherenkov detector, law.invention, Nuclear physics, Nuclear Energy and Engineering, law, Measuring instrument, High Energy Physics::Experiment, Electrical and Electronic Engineering, computer, Delphi, computer.programming_language
Abstract

The Forward Ring Imaging Cherenkov detector covers both end-cap regions of the DELPHI experiment at LEP in the polar angle 15/spl deg/ >

Published
1994

27. The forward ring imaging cherenkov detector of DELPHI

Author

W. Dulinski, K. Pakonski, F. Montano, Andre Augustinus, E. Karvelas, P. Kindblom, N. Dimitriou, A.P. Budziak, B. Lund-Jensen, P. Carecchio, A. Korporaal, E. Zevgolatakos, D. Isenhower, S. Squarcia, Tord Ekelof, Borge Svane Nielsen, T. Henkes, O. Ullaland, Håkan T Johansson, C. Perdikis, E. Saragas, E. Schyns, E. Dahl-Jensen, Mario Sannino, L. O. Eek, G. Lenzen, Wolfgang Adam, Jorge-Armando Benitez Garcia, A. Fontanelle, C. Barnoux, F. Fontanelli, B. Koene, A. Robohm, R. Cereseto, G. Polok, A. Wärm, A. Florek, G. Damgaard, P. Cavalli, Allan Hallgren, A.S. de la Vega, L.-E. Lindqvist, N. de Koning, L. Mattsson, G. E. Theodosiou, J.M. Ostler, E. Albrecht, K. Galuszka, V. Gracco, J. Erikson, L. Ceelie, J. Werner, Marek Stodulski, G. Sajot, J. Thadome, M. Zavrtanik, A. Maltezos, B. Boštjančič, J. Medbo, P. Lorenz, P. Dam, Panos Kostarakis, Demetrios Loukas, Z. Stopa, W. Hao, Luiz Caloba, I. Ambec, L. Traspedini, Guillaume Cerutti, Olga Botner, A. Markou, B. Florek, Jerzy Michalowski, Georgios Stavropoulos, M. Turala, S. Xyroutsikos, Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches Subatomiques (IReS), and Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Cancéropôle du Grand Est-Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS)

Subjects
Physics, Nuclear and High Energy Physics, Photon, Physics::Instrumentation and Detectors, 010308 nuclear & particles physics, business.industry, Cherenkov detector, Detector, Photodetector, 01 natural sciences, Particle detector, Particle identification, Ring-imaging Cherenkov detector, law.invention, Nuclear physics, Optics, law, 0103 physical sciences, High Energy Physics::Experiment, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Detectors and Experimental Techniques, 010306 general physics, business, Instrumentation, Cherenkov radiation
Abstract

The Forward Ring Imaging Cherenkov detector of the DELPHI experiment at LEP provides hadron identification at polar angles15° < θ < 35° and 145° < θ < 165°. Two radiator media, a layer of liquid C6F14 and a volume of gaseous C4F10, in combination provide coverage of momenta up to 40 GeV/c. A single array of photosensitive Time Projection Chambers registers the impact points of ultraviolet photons from both radiators. The design of the detector and of its readout system is described. First results obtained with a partly installed detector are reported.

Published
1994

28. Performance of the forward RICH detector system at DELPHI

Author

T. Henkes, G. Lenzen, P. Cavalli, O. Ullaland, A. Markou, B. Koene, V. Gracco, L. Traspedini, Jorge-Armando Benitez Garcia, E. Schyns, Z. Stopa, J. Erikson, J. Medbo, R. Cereseto, M. Turala, Mario Sannino, A.S. de la Vega, L.-E. Lindqvist, P. Carechio, E. Albrecht, Georgios Stavropoulos, L. O. Eek, Guillaume Cerutti, S. Xyroutsikos, A. Wärm, A.P. Budziak, K. Pakonski, F. Fontanelli, H. Johansson, Jürgen H. Werner, Borge Svane Nielsen, M. Zavrtanik, F. Montano, Z. Thome, P. Lorenz, Tord Ekelof, E. Karvelas, W. Hao, N. Dimitriou, Bengt Lund-Jensen, B. Florek, Jerzy Michalowski, I. Ambec, A. Robohm, Marek Stodulski, G. Sajot, N. de Koning, W. Dulinski, A. Squarcia, O. Botner, A. Maltezos, C. Barnoux, L. Ceelie, Allan Hallgren, J. Thadome, C. Perdikes, Andre Augustinus, A. Korporaal, Demetrios Loukas, B. Boštjančič, Panos Kostarakis, A. Florek, G. Damgaard, E. Dahl-Jensen, K. Galuszka, Piet Van Dam, P. Kindblom, L. Mattsson, A. Fontenille, E. Zevgolatakos, D. Isenhower, Wolfgang Adam, E. Saragas, G. E. Theodosiou, G. Polok, bibliotheque, LPSC, Laboratoire de Physique Subatomique et de Cosmologie (LPSC), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)

Subjects
Nuclear and High Energy Physics, Physics::Instrumentation and Detectors, 01 natural sciences, Particle identification, Particle detector, law.invention, Optics, law, 0103 physical sciences, Electrical and Electronic Engineering, 010306 general physics, Ultraviolet radiation, Cherenkov radiation, computer.programming_language, Physics, 010308 nuclear & particles physics, business.industry, Detector, Particle accelerator, Nuclear Energy and Engineering, Measuring instrument, Optoelectronics, High Energy Physics::Experiment, business, computer, Delphi
Abstract

One quarter of the Forward Ring Imaging Cherenkov (Forward RICH) detector has been installed and operated in the DELPHI experiment. The detector covers the forward-backward regions (15 degrees >

Published
1993

29. A Model-Based Approach for Compound Leaves Understanding and Identification

Author

Guillaume Cerutti, Antoine Vacavant, Julien Mille, Didier Coquin, Laure Tougne, Geometry Processing and Constrained Optimization (M2DisCo), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Extraction de Caractéristiques et Identification (imagine), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), and Cerutti, Guillaume

Subjects
Computer science, Semantic interpretation, compound leaf, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Interpretation (model theory), Set (abstract data type), plant recognition, 0202 electrical engineering, electronic engineering, information engineering, Segmentation, image segmentation, deformable templates, Contextual image classification, business.industry, 020207 software engineering, Pattern recognition, Image segmentation, 15. Life on land, active contours, Identification (information), [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], classification, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

International audience; In this paper, we propose a specific method for the identification of compound-leaved tree species, with the aim of integrating it in an educational smartphone application. Our work is based on dedicated shape models for compound leaves, designed to estimate the number and shape of leaflets. A deformable template approach is used to fit these models and produce a high-level interpretation of the image content. The resulting models are later used for the segmentation of leaves in both plain and natural background images, by the use of multiple region-based active contours. Combined with other botany-inspired descriptors accounting for the morphological properties of the leaves, we propose a classification method that makes a semantic interpretation possible. Results are presented over a set of more than 1000 images from 17 European tree species, and an integration in the existing mobile application Folia is considered.

30. Image analysis for multi-organ ofplantspecies identification

Author

Bertrand, Sarah, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Extraction de Caractéristiques et Identification (imagine), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon, Laure Tougne, Guillaume Cerutti, and BERTRAND, Sarah

Subjects
Segmentation, Fusion de données, [INFO.EIAH] Computer Science [cs]/Technology for Human Learning, Extraction de descripteurs, Descriptor extraction, Reconnaissance de plantes, Plant recognition, Réseaux de neurones, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Data fusion, Classification, Neural networks
Abstract

This thesis is part of the ANR ReVeRIES, which aims to use mobile technologies to help people better understand their environment and in particular the plants that surround them. More precisely, the ReVeRIES project is based on a mobile application called Folia developed as part of the ANR ReVeS project and capable of recognising tree and shrub species based on photos of their leaves. This prototype differs from other tools in that it is able to simulate the behaviour of the botanist. In the context of the ReVeRIES project, we propose to go much further by developing new aspects: multimodal species recognition, learning through play and citizen science. The purpose of this thesis is to focus on the first of these three aspects, namelythe analysis of images of plant organs for identification.More precisely, we consider the main trees and shrubs, endemic or exotic, found in metropolitan France. The objective of this thesis is to extend the recognition algorithm by taking into account other organs in addition to the leaf. This multi-modality is indeed essential if we want the user to learn and practice the different methods of recognition for which botanists use the variety of organs (i.e. leaves, flowers, fruits and bark). The method used by Folia for leaf recognition being dedicated, because simulating the work of a botanist on the leaf, cannot be applied directly to other organs. Thus, new challenges are emerging, both in terms of image processing and data fusion.The first part of the thesis was devoted to the implementation of image processing methods for the identification of plant species. The identification of tree species from bark images was the first to be studied. The descriptors developed take into account the structure of the bark inspired from the criteria used by botanists. Fruits and flowers required a segmentation step before their description. A new segmentation method that can be used on smartphones has been developed to work in spite of the high variability of flowers and fruits. Finally, descriptors were extracted on fruits and flowers after the segmentation step. We decided not to separate flowers and fruits because we showed that a user new to botany does not always know the difference between these two organs on so-called "ornamental" trees (not fruit trees). For fruits and flowers, prediction is not only made on their species but also on their genus and family, botanical groups reflecting a similarity between these organs.The second part of the thesis deals with the combination of descriptors of the different organs: leaves, bark, fruits and flowers. In addition to basic combination methods, we propose to consider the confusion between species, as well as predictions of affiliations in botanical taxa higher than the species.Finally, an opening chapter is devoted to the processing of these images by convolutional neural networks. Indeed, Deep Learning is increasingly used in image processing, particularly for plant organs. In this context, we propose to visualize the learned convolution filters extracting information, in order to make the link between the information extracted by these networks and botanical elements., Cette thèse s’inscrit dans le cadre de l’ANR ReVeRIES dont l’objectif est d’utiliser les technologies mobiles pour aider l’homme à mieux connaître son environnement et notamment les végétaux qui l’entourent. Plus précisément, le projet ReVeRIES s’appuie sur une application mobile, nommée Folia développée dans le cadre du projet ANR ReVeS, capable de reconnaître les espèces d’arbres et arbustes à partir de photos de leurs feuilles. Ce prototype se différencie des autres outils car il est capable de simuler le comportement du botaniste. Dans le contexte du projet ReVeRIES, nous nous proposons d’aller beaucoup plus loin en développant de nouveaux aspects : la reconnaissance multimodale d’espèces, l’apprentissage par le jeu et les sciences citoyennes. L’objet de cette thèse porte sur le premier de ces trois aspects, à savoir l’analyse d’images d’organes de végétaux en vue de l’identification.Plus précisément, nous considérons les principaux arbres et arbustes, endémiques ou exotiques, que l’on trouve en France métropolitaine. L’objectif de cette thèse est d’étendre l’algorithme de reconnaissance en prenant en compte d’autres organes que la feuille. Cette multi-modalité est en effet essentielle si nous souhaitons que l’utilisateur apprenne et s’entraîne aux différentes méthodes de reconnaissance, pour lesquelles les botanistes utilisent la variété des organes (i.e. les feuilles, les fleurs, les fruits et les écorces). La méthode utilisée par Folia pour la reconnaissance des feuilles étant dédiée, car simulant le botaniste, ne peut s’appliquer directement aux autres organes. Ainsi, de nouveaux verrous se posent, tant au niveau dutraitement des images qu’au niveau de la fusion de données.Une première partie de la thèse a été consacrée à la mise en place de méthodes de traitement d’images pour l’identification des espèces végétales. C’est l’identification des espèces d’arbres à partir d’images d’écorces qui a été étudiée en premier. Les descripteurs développés prennent en compte la structure de l’écorce en s’inspirant des critères utilisés par les botanistes. Les fruits et les fleurs ont nécessité une étape de segmentation avant leur description. Une nouvelle méthode de segmentation réalisable sur smartphone a été développée pour fonctionner sur la grande variabilité des fleurs et des fruits. Enfin, des descripteurs ont été extraits sur les fruits et les fleurs après l’étape de segmentation. Nous avons décidé de ne pas faire de séparation entre les fleurs et les fruits car nous avons montré qu’un utilisateur novice en botanique ne sait pas toujours faire la différence entre ces deux organes sur des arbres dits «d’ornement» (non fruitiers). Pour les fruits et les fleurs, la prédiction n’est pas seulement faite sur les espèces mais aussi sur les genres et les familles, groupes botaniques traduisant d’une similarité entre ces organes.Une deuxième partie de la thèse traite de la combinaison des descripteurs des différents organes que sont les feuilles, les écorces, les fruits et les fleurs. En plus des méthodes de combinaison basiques, nous proposons de prendre en compte la confusion entre les espèces, ainsi que les prédictions d’appartenance aux taxons botaniques supérieurs à l’espèce.Enfin, un chapitre d’ouverture est consacré au traitement de ces images par des réseaux de neurones à convolutions. En effet, le Deep-Learning est de plus en plus utilisé en traitement d’images, notamment appliqué aux organes végétaux. Nous proposons dans ce contexte de visualiser les filtres de convolution extrayant de l’information, afin de faire le lien entre lesinformations extraites par ces réseaux et les éléments botaniques.

Published
2018

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