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6 results on '"Ahlem Othmani"'

1. Multi-layer Ontologies for Integrated 3D Shape Segmentation and Annotation

Author

Fakhri Torkhani, Thomas Dietenbeck, Marco Attene, Jean-Marie Favreau, Ahlem Othmani, 2 - Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-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 ) -Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ), Image Science for Interventional Techniques ( ISIT ), Université d'Auvergne - Clermont-Ferrand I ( UdA ) -Clermont Université-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), IMATI, Istituto di Matematica Applicata e Tecnologie Informatiche ( IMATI-CNR ), Consiglio Nazionale delle Ricerche [Roma] ( CNR ) -Consiglio Nazionale delle Ricerche [Roma] ( CNR ), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes ( LIMOS ), Sigma CLERMONT ( Sigma CLERMONT ) -Université Clermont Auvergne ( UCA ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-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), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Istituto di Matematica Applicata e Tecnologie Informatiche (IMATI-CNR), Consiglio Nazionale delle Ricerche [Roma] (CNR)-Consiglio Nazionale delle Ricerche [Roma] (CNR), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Favreau, Jean-Marie, Laboratoire d'Imagerie Biomédicale [Paris] (LIB), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR)-National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), and Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Image processing, 02 engineering and technology, Geometry processing, Ontology (information science), computer.software_genre, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Expert system, Annotation, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG], N/A, 0202 electrical engineering, electronic engineering, information engineering, [ INFO.INFO-CG ] Computer Science [cs]/Computational Geometry [cs.CG], Preprocessor, Domain knowledge, 020201 artificial intelligence & image processing, Segmentation, Data mining, computer
Abstract

Mesh segmentation and semantic annotation are used as preprocessing steps formany applications, including shape retrieval, mesh abstraction, and adaptive simplification. In current practice, these two steps are done sequentially: a purely geometrical analysis is employed to extract the relevant parts, and then these parts are annotated. We introduce an original framework where annotation and segmentation are performed simultaneously, so that each of the two steps can take advantage of the other. Inspired by existing methods used in image processing, we employ an expert's knowledge of the context to drive the process while minimizing the use of geometric analysis. For each specific context a multi-layer ontology can be designed on top of a basic knowledge layer which conceptualizes 3D object features from the point of view of their geometry, topology, and possible attributes. Each feature is associated with an elementary algorithm for its detection. An expert can define the upper layers of the ontology to conceptualize a specific domain without the need to reconsider the elementary algorithms. This approach has a twofold advantage: on one hand it allows Mesh segmentation and semantic annotation are used as preprocessing steps formany applications, including shape retrieval, mesh abstraction, and adaptive simplification. In current practice, these two steps are done sequentially: a purely geometrical analysis is employed to extract the relevant parts, and then these parts are annotated. We introduce an original framework where annotation and segmentation are performed simultaneously, so that each of the two steps can take advantage of the other. Inspired by existing methods used in image processing, we employ an expert's knowledge of the context to drive the process while minimizing the use of geometric analysis. For each specific context a multi-layer ontology can be designed on top of a basic knowledge layer which conceptualizes 3D object features from the point of view of their geometry, topology, and possible attributes. Each feature is associated with an elementary algorithm for its detection. An expert can define the upper layers of the ontology to conceptualize a specific domain without the need to reconsider the elementary algorithms. This approach has a twofold advantage: on one hand it allows Mesh segmentation and semantic annotation are used as preprocessing steps formany applications, including shape retrieval, mesh abstraction, and adaptive simplification. In current practice, these two steps are done sequentially: a purely geometrical analysis is employed to extract the relevant parts, and then these parts are annotated. We introduce an original framework where annotation and segmentation are performed simultaneously, so that each of the two steps can take advantage of the other. Inspired by existing methods used in image processing, we employ an expert's knowledge of the context to drive the process while minimizing the use of geometric analysis. For each specific context a multi-layer ontology can be designed on top of a basic knowledge layer which conceptualizes 3D object features from the point of view of their geometry, topology, and possible attributes. Each feature is associated with an elementary algorithm for its detection. An expert can define the upper layers of the ontology to conceptualize a specific domain without the need to reconsider the elementary algorithms. This approach has a twofold advantage: on one hand it allows to leverage domain knowledge from experts even if they have limited or no skills in geometry processing and computer programming; on the other hand, it provides a solid ground to be easily extended in different contexts with a limited effort.

Published
2016
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2. Tree Species Classification Based on 3D Bark Texture Analysis

Author

Lew F.C. Lew Yan Voon, Alexandre Piboule, Nicolas Loménie, Oscar Dalmau, Said Mokrani, Ahlem Othmani, Laboratoire Electronique, Informatique et Image [UMR6303] ( Le2i ), Centre National de la Recherche Scientifique ( CNRS ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-École Nationale Supérieure d'Arts et Métiers ( ENSAM ), Image Science for Interventional Techniques ( ISIT ), Université d'Auvergne - Clermont-Ferrand I ( UdA ) -Clermont Université-Centre National de la Recherche Scientifique ( CNRS ), Image & Pervasive Access Lab ( IPAL ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -National University of Singapore ( NUS ) -MATHEMATIQUES, SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (UJF)-Agency for science, technology and research [Singapore] ( A*STAR ) -Centre National de la Recherche Scientifique ( CNRS ) -Institute for Infocomm Research - I²R [Singapore], Office National des Forêts - ONF (FRANCE), Centro de Investigación en Matemáticas ( CIMAT ), Consejo Nacional de Ciencia y Tecnología [Mexico] ( CONACYT ), Centre de Recherche en Informatique de Paris 5 ( CRIP5 - EA 2517 ), Université Paris Descartes - Paris 5 ( UPD5 ), Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université, Image & Pervasive Access Lab (IPAL), Institute for Infocomm Research - I²R [Singapore]-Centre National de la Recherche Scientifique (CNRS)-Agency for science, technology and research [Singapore] (A*STAR)-MATHEMATIQUES, SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (UJF)-National University of Singapore (NUS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Office National des Forêts (ONF), Centro de Investigación en Matemáticas (CIMAT), Consejo Nacional de Ciencia y Tecnología [Mexico] (CONACYT), Centre de Recherche en Informatique de Paris 5 (CRIP5 - EA 2517), Université Paris Descartes - Paris 5 (UPD5), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre et Marie Curie - Paris 6 (UPMC)-National University of Singapore (NUS)-Agency for science, technology and research [Singapore] (A*STAR)-Centre National de la Recherche Scientifique (CNRS)-Institute for Infocomm Research - I²R [Singapore], Office national des forêts (ONF), and Clermont Université-Centre National de la Recherche Scientifique (CNRS)-Université d'Auvergne - Clermont-Ferrand I (UdA)

Subjects
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Computer science, Point cloud, 02 engineering and technology, computer.software_genre, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Texture (geology), 3D pattern recognition, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, 0202 electrical engineering, electronic engineering, information engineering, forest inventory, [STAT.AP]Statistics [stat]/Applications [stat.AP], Forest inventory, Trees pecies classification, [ STAT.AP ] Statistics [stat]/Applications [stat.AP], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 3D bark texture analysis, 020206 networking & telecommunications, Terrestrial laser scanning, 15. Life on land, Random forest, visual_art, Test set, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], visual_art.visual_art_medium, 020201 artificial intelligence & image processing, Bark, Data mining, computer, Tree species
Abstract

Terrestrial Laser Scanning (TLS) technique is today widely used in ground plots to acquire 3D point clouds from which forest inventory attributes are calculated. In the case of mixed plantings where the 3D point clouds contain data from several different tree species, it is important to be able to automatically recognize the tree species in order to analyze the data of each of the species separately. Although automatic tree species recognition from TLS data is an important problem, it has received very little attention from the scientific community. In this paper we propose a method for classifying five different tree species using TLS data. Our method is based on the analysis of the 3D geometric texture of the bark in order to compute roughness measures and shape characteristics that are fed as input to a Random Forest classifier to classify the tree species. The method has been evaluated on a test set composed of 265 samples (53 samples of each of the 5 species) and the results obtained are very encouraging.

Published
2013
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3. Region-based segmentation on depth images from a 3D reference surface for tree species recognition

Author

Christophe Stolz, Lew F.C. Lew Yan Voon, Ahlem Othmani, Alexandre Piboule, Nicolas Loménie, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire d'Informatique Paris Descartes (LIPADE - EA 2517), Université Paris Descartes - Paris 5 (UPD5), Département R&D, Office National des Forêts (ONF), Othmani, Alice Ahlem, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Informatique Paris Descartes ( LIPADE - EA 2517 ), Université Paris Descartes - Paris 5 ( UPD5 ), Office National des Forêts - ONF (FRANCE), Office National des Forêts - ONF (FRANCE)-Office National des Forêts - ONF (FRANCE), Laboratoire Electronique, Informatique et Image [UMR6303] ( Le2i ), and Centre National de la Recherche Scientifique ( CNRS ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-École Nationale Supérieure d'Arts et Métiers ( ENSAM )

Subjects
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Feature extraction, Point cloud, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-space segmentation, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, 02 engineering and technology, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Minimum spanning tree-based segmentation, [STAT.AP] Statistics [stat]/Applications [stat.AP], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Segmentation, Computer vision, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, [STAT.AP]Statistics [stat]/Applications [stat.AP], Contextual image classification, business.industry, [ STAT.AP ] Statistics [stat]/Applications [stat.AP], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Pattern recognition, Image segmentation, 15. Life on land, depth image segmentation, Random forest, depth images from 3D point clouds, IEEE, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, single tree species recognition, Artificial intelligence, Range segmentation, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Forest inventory
Abstract

International audience; The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has been evaluated using two datasets acquired in two different French forests with different terrain characteristics. The results obtained are very encouraging and promising.

Published
2013

4. Hybrid segmentation of depth images using a watershed and region merging based method for tree species recognition

Author

Alexandre Piboule, Lew F.C. Lew Yan Voon, Ahlem Othmani, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-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), Image & Pervasive Access Lab (IPAL), Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre et Marie Curie - Paris 6 (UPMC)-National University of Singapore (NUS)-Agency for science, technology and research [Singapore] (A*STAR)-Centre National de la Recherche Scientifique (CNRS)-Institute for Infocomm Research - I²R [Singapore], Office national des forêts (ONF), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université, Institute for Infocomm Research - I²R [Singapore]-Centre National de la Recherche Scientifique (CNRS)-Agency for science, technology and research [Singapore] (A*STAR)-MATHEMATIQUES, SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (UJF)-National University of Singapore (NUS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Office National des Forêts (ONF), Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-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 ), Image & Pervasive Access Lab ( IPAL ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -National University of Singapore ( NUS ) -MATHEMATIQUES, SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (UJF)-Agency for science, technology and research [Singapore] ( A*STAR ) -Centre National de la Recherche Scientifique ( CNRS ) -Institute for Infocomm Research - I²R [Singapore], and Office National des Forêts - ONF (FRANCE)

Subjects
Watershed, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Point cloud, 02 engineering and technology, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 01 natural sciences, 010309 optics, 3D geometric texture classification, Image texture, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0103 physical sciences, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Segmentation, Computer vision, [STAT.AP]Statistics [stat]/Applications [stat.AP], Forest inventory, Contextual image classification, business.industry, [ STAT.AP ] Statistics [stat]/Applications [stat.AP], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Image segmentation, 15. Life on land, Random forest, Geography, Index Terms— Forest inventory, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, single tree species recognition, Artificial intelligence, terrestrial laser scanning, business
Abstract

International audience; Tree species recognition from Terrestrial Light Detection and Ranging (T-LiDAR) scanner data is essential for estimating forest inventory attributes in a mixed planting. In this paper, we propose a new method for individual tree species recognition based on the analysis of the 3D geometric texture of tree barks. Our method transforms the 3D point cloud of a 30 cm segment of the tree trunk into a depth image on which a hybrid segmentation method using watershed and region merging techniques is applied in order to reveal bark shape characteristics. Finally, shape and intensity features are calculated on the segmented depth image and used to classify five different tree species using a Random Forest (RF) classifier. Our method has been tested using two datasets acquired in two different French forests with different terrain characteristics. The accuracy and precision rates obtained for both datasets are over 89%.

Published
2013
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5. Towards automated and operational forest inventories with T-Lidar

Author

Ahlem Othmani, Alexandre Piboule, Krebs, M., Stolz, C., Lew LEW YAN VOON, Othmani, Alice Ahlem, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, ONF R&D department (ONF), ONF, ENSAM Cluny Equipe Bois (ENSAM), ENSAM Cluny, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), ONF R&D department ( ONF ), and ENSAM Cluny Equipe Bois ( ENSAM )

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, DBH, [ STAT.AP ] Statistics [stat]/Applications [stat.AP], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [STAT.AP] Statistics [stat]/Applications [stat.AP], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, terrestrial laser scanning, forest inventory, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, tree detection, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Forest inventory automation has become a major issue in forestry. The complexity of the segmentation of 3D point cloud is due to mutual occlusion between trees, other vegetation, or branches. That is why, the applications done until now are limited to the estimation of the DBH (Diameter at Breast Height), the tree height and density estimation. Furthermore other parameters could also be detected, such as volume or species of trees (Reulke and Haala) . . . This paper presents an effective approach for automatic detection, isolation of trees and DBH estimation. Tree isolation is achieved using an innovative approach based on a clustering methodology followed by a skeletonization step. The DBH of trees is then determined automatically. The efficiency of our algorithm is evaluated with comparison with ground data, measured by classical methods.

Published
2011

6. Ontology-driven Image Analysis for Histopathological Images

Author

Nicolas Loménie, Ahlem Othmani, Carole Meziat, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Image & Pervasive Access Lab ( IPAL ), National University of Singapore ( NUS ) -MATHEMATIQUES, SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (UJF)-Agency for science, technology and research [Singapore] ( A*STAR ) -Centre National de la Recherche Scientifique ( CNRS ) -Institute for Infocomm Research - I²R [Singapore], Centre de Recherche en Informatique de Paris 5 ( CRIP5 - EA 2517 ), Université Paris Descartes - Paris 5 ( UPD5 ), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Image & Pervasive Access Lab (IPAL), National University of Singapore (NUS)-MATHEMATIQUES, SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (UJF)-Agency for science, technology and research [Singapore] (A*STAR)-Centre National de la Recherche Scientifique (CNRS)-Institute for Infocomm Research - I²R [Singapore], Centre de Recherche en Informatique de Paris 5 (CRIP5 - EA 2517), Université Paris Descartes - Paris 5 (UPD5), Othmani, Alice Ahlem, Laboratoire Electronique, Informatique et Image [UMR6303] ( Le2i ), and Centre National de la Recherche Scientifique ( CNRS ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-École Nationale Supérieure d'Arts et Métiers ( ENSAM )

Subjects
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image processing, Ontology (information science), [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 030218 nuclear medicine & medical imaging, 03 medical and health sciences, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, Software, [STAT.AP] Statistics [stat]/Applications [stat.AP], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Digital image processing, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Computer vision, RDF, Image analysis, [STAT.AP]Statistics [stat]/Applications [stat.AP], Information retrieval, [ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging, business.industry, [ STAT.AP ] Statistics [stat]/Applications [stat.AP], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Usability, computer.file_format, Automatic image annotation, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], 030220 oncology & carcinogenesis, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, business, computer
Abstract

International audience; Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images. In addition to specific low-level image analysis procedures, we used knowledge formalization tools and high-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibility of the low-level image analysis algorithms.

Published
2010

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