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14 results on '"Rémi Ratajczak"'

6. Tools Against Oblivion: 3D Visualization of Sunken Landscapes and Cultural Heritages Applied to a Dam Reservoir in the Gorges de la Loire (France)

Author

Pierre Niogret, Pierre-Olivier Mazagol, Rémi Ratajczak, Michel Depeyre, Jérémie Riquier, Carlos Crispim-Junior, Laure Tougne, Environnement, Ville, Société (EVS), École normale supérieure de Lyon (ENS de Lyon)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), 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)-Université Jean Monnet - Saint-Étienne (UJM)-École Nationale des Travaux Publics de l'État (ENTPE)-École nationale supérieure d'architecture de Lyon (ENSAL)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet - Saint-Étienne (UJM), 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-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), Environnement Ville Société (EVS), 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)-École nationale supérieure d'architecture de Lyon (ENSAL)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Nationale des Travaux Publics de l'État (ENTPE)-Université Jean Monnet [Saint-Étienne] (UJM)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université Lumière - Lyon 2 (UL2)-École normale supérieure - Lyon (ENS Lyon), Université Jean Monnet [Saint-Étienne] (UJM), 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
Geographic information system, business.industry, Geography, Planning and Development, Flooding (psychology), Environmental resource management, Visibility (geometry), 0211 other engineering and technologies, 021107 urban & regional planning, 02 engineering and technology, [SHS.GEO]Humanities and Social Sciences/Geography, 010501 environmental sciences, 15. Life on land, 01 natural sciences, [SDE.ES]Environmental Sciences/Environmental and Society, Visualization, Cultural heritage, Backup, Anthropocene, Earth and Planetary Sciences (miscellaneous), Geovisualization, Computers in Earth Sciences, business, [SHS.HIST]Humanities and Social Sciences/History, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

Around the world, thousands of reservoir dams have flooded valleys and become concrete symbols of the Anthropocene. These landscapes, as well as material or immaterial cultural heritages, were flooded and thus became invisible, even though they remain in the memories of local populations. Today, alternative technologies can enable inhabitants to reappropriate these lost heritages and, in a way, make them visible again. 3D digital tools can effectively recreate representations of these landscapes and restore the visibility of these underwater heritage sites. In this study, we propose a 3D geographic information system methodology combined with 3D geovisualization to recreate sunken landscapes, and we demonstrate the results using the valley of the Gorges de la Loire in France as an example. We show how developing a historical database can provide a “backup copy” of lost landscapes and cultural heritage sites, enabling populations to safeguard and restore these features in their memory following flooding.

Published
2021

7. Semantic Segmentation Post-processing with Colorized Pairwise Potentials and Deep Edges

Author

Laure Tougne, Elodie Faure, Rémi Ratajczak, Carlos Crispim, Béatrice Fervers, Extraction de Caractéristiques et Identification (imagine), 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), Département cancer environnement (Centre Léon Bérard - Lyon), Centre Léon Bérard [Lyon], Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), LABEX IMU (ANR-10-LABX-0088/ ANR-11-IDEX-0007)., ADEME (N TEZ17-42), Centre Léon Bérard, IEEE, TESTIS, and IMU GOURAMIC

Subjects
Conditional random field, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, 02 engineering and technology, Grayscale, Deep Edges, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Segmentation, Deep Learning, Land Use, 0202 electrical engineering, electronic engineering, information engineering, 021101 geological & geomatics engineering, Pixel, business.industry, Deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Object (computer science), [SDE.ES]Environmental Sciences/Environmental and Society, Panchromatic film, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Pairwise comparison, Artificial intelligence, business
Abstract

International audience; Semantic segmentation is the task of assigning a label to each pixel in an image, providing high level insights to a wide range of end-user applications like autonomous driving, medical imaging and land use mapping. However, semantic segmentation results are not always consistent with the object boundaries and may sometimes lack of spatial consistency. To solve these problems, post-processing algorithms have been proposed, paving the way for more robust pipelines. In this work, we study a novel post-processing approach to enhance semantic segmentation of panchromatic aerial images based on unsupervised colorization and deep edge superpixels. In particular, we propose to assess whether applying a colorization algorithm could enhance the strength of the pairwise potentials used in an extended dense conditional random field. We present experiments on recent aerial color images that we convert to grayscale before colorization, allowing us to assess how colorized representations impact post-processing when compared to real color and panchromatic representations.

Published
2020

8. Semantic Segmentation Refinement with Deep Edge Superpixels to Enhance Historical Land Cover

Author

Laure Tougne, Rémi Ratajczak, Béatrice Fervers, Carlos Crispim-Junior, Elodie Faure, Extraction de Caractéristiques et Identification (imagine), 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), Département cancer environnement (Centre Léon Bérard - Lyon), Centre Léon Bérard [Lyon], Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), LABEX IMU (ANR-10-LABX-0088/ ANR-11-IDEX-0007)., ADEME (N TEZ17-42), Centre Léon Bérard, IEEE, TESTIS, IMU GOURAMIC, 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), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)

Subjects
Conditional random field, Computer science, 02 engineering and technology, Land cover, 010501 environmental sciences, 01 natural sciences, Deep Edges, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Segmentation, Deep Learning, Land Use, 0202 electrical engineering, electronic engineering, information engineering, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, business.industry, Deep learning, Land use land cover, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Image segmentation, 15. Life on land, Classification, [SDE.ES]Environmental Sciences/Environmental and Society, Historical Land Use, Kernel (image processing), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

International audience; In this work, we explore a post-processing method to enhance coarse semantic segmentation of historical aerial images. We propose to use deep edges to generate semantically meaningful superpixels that we integrate as additional pairwise potentials in a dense conditional random field. We apply our approach on very high resolution images acquired between 1975 and 1995 and annotated with land use land cover labels. Results show the interest of our approach compared to other post-processing methods.

Published
2020

9. Toward An Unsupervised Colorization Framework for Historical Land Use Classification

Author

Béatrice Fervers, Laure Tougne, Rémi Ratajczak, Carlos Crispim-Junior, Elodie Faure, Extraction de Caractéristiques et Identification (imagine), 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), Département cancer environnement (Centre Léon Bérard - Lyon), Centre Léon Bérard [Lyon], Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), LABEX IMU (ANR-10-LABX-0088/ ANR-11-IDEX-0007)., ADEME (N TEZ17-42), Centre Léon Bérard, IEEE, TESTIS, and IMU GOURAMIC

Subjects
Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Texture (geology), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Deep Learning, Land Use, 0202 electrical engineering, electronic engineering, information engineering, Colorization, Land use, Artificial neural network, Texture filters, business.industry, Deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, 15. Life on land, Index Terms-Colorization, Classification, [SDE.ES]Environmental Sciences/Environmental and Society, GAN, Visualization, Historical Land Use, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

International audience; We present an unsupervised colorization framework to improve both the visualization and the automatic land use classification of historical aerial images. We introduce a novel algorithm built upon a cyclic generative adversarial neural network and a texture replacement method to homogeneously and automatically colorize unpaired VHR images. We apply our framework on historical aerial images acquired in France between 1970 and 1990. We demonstrate that our approach helps to disentangle hard to classify land use classes and hence improves the overall land use classification.

Published
2019

10. Efficient Bark Recognition in the Wild

Author

Laure Tougne, Rémi Ratajczak, Carlos Crispim-Junior, Sarah Bertrand, Extraction de Caractéristiques et Identification (imagine), 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), Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), Département cancer environnement (Centre Léon Bérard - Lyon), Centre Léon Bérard [Lyon], and ANR-15-CE38-0004,ReVeRIES,Reconnaissance de Végétaux Récréative, Interactive et Educative sur Smartphone(2015)

Subjects
Computer science, Feature vector, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Color space, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Margin (machine learning), Histogram, Prior probability, 0202 electrical engineering, electronic engineering, information engineering, color quantification, Dimensionality Reduction, business.industry, Dimensionality reduction, Bark recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Pattern recognition, Sensor fusion, Data Fusion, visual_art, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], visual_art.visual_art_medium, Bark, Features Extraction, texture classification, Artificial intelligence, business, Color and Texture Analyses
Abstract

International audience; In this study, we propose to address the difficult task of bark recognition in the wild using computationally efficient and compact feature vectors. We introduce two novel generic methods to significantly reduce the dimensions of existing texture and color histograms with few losses in accuracy. Specifically, we propose a straightforward yet efficient way to compute Late Statistics from texture histograms and an approach to iteratively quantify the color space based on domain priors. We further combine the reduced histograms in a late fusion manner to benefit from both texture and color cues. Results outperform state-of-the-art methods by a large margin on four public datasets respectively composed of 6 bark classes (BarkTex, NewBarkTex), 11 bark classes (AFF) and 12 bark classes (Trunk12). In addition to these experiments, we propose a baseline study on Bark-101 (http://eidolon.univ-lyon2.fr/~remi1/Bark-101/), a new challenging dataset including manually segmented images of 101 bark classes that we release publicly.Bark-101: http://eidolon.univ-lyon2.fr/~remi1/Bark-101/

Published
2019

11. Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

Author

Rémi Ratajczak, Laure Tougne, Béatrice Fervers, Elodie Faure, Carlos Crispim-Junior, Extraction de Caractéristiques et Identification (imagine), 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), Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), Département cancer environnement (Centre Léon Bérard - Lyon), Centre Léon Bérard [Lyon], ADEME (N TEZ17-42), ENVITERA, CENTRE LEON BERARD, LABEX IMU (ANR-10-LABX-0088/ ANR11-IDEX-0007), TESTIS, and IMU GOURAMIC

Subjects
Local binary patterns, Feature vector, Index Terms-Features extraction, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Convolutional neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, Deep Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Deep Con- volutional Neural Networks, business.industry, Texture filters, Deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Image segmentation, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Land Cover, Computer Graphics and Computer-Aided Design, Historical Aerial Images, Statistical classification, Filter (video), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Artificial intelligence, business, Software
Abstract

International audience; The land cover reconstruction from monochromatic historical aerial images is a challenging task that has recently known an increasing interest from the scientific community with the proliferation of large scale epidemiological studies involving retrospective analysis of spatial pattern. However, the efforts engaged by the computer vision community in remote sensing applications are mostly focused on prospective approaches through the analysis of high resolution multi-spectral data acquired by advanced spatial programs. Hence, four contributions are proposed in this article. They aim at providing a comparison basis for the future development of computer vision algorithms applied to the automation of the land cover reconstruction from monochromatic historical aerial images. Firstly, a new multiscale multi-date dataset composed of 4.9 million non-overlapping annotated patches of the France territory between 1970 and 1990 has been created with the help of Geography experts. This dataset has been named HistAerial (http://eidolon.univ-lyon2.fr/~remi1/HistAerialDataset/). Secondly, an extensive comparison study of state-of-the-art texture features extraction and classification algorithms including deep convolutional neural networks (DCNNs) has been performed. It is presented in the form of an evaluation. Thirdly, a novel low-dimensional local texture filter named Rotated-CorneR Local Binary Pattern (RCRLBP) is presented as a simplification of the Binary Gradient Contours filter through the use of an orthogonal combination representation. Finally, a novel combination of low-dimensional texture descriptors, including the R-CRLBP filter, is introduced as a Light Combination of Local Binary Patterns (LCoLBP). The LCoLBP filter achieved state-of-the-art results on the HistAerial dataset while conserving a relatively low-dimensional feature vector space compared with the DCNN approaches (17 times shorter).HistAerial : http://eidolon.univ-lyon2.fr/~remi1/HistAerialDataset/

Published
2019

12. GOURAMIC: A Software to Estimate Historical Land Use in Epidemiological Studies

Author

Laure Tougne, Carlos Crispim-Junior, Béatrice Fervers, Aurélie M N Danjou, Elodie Faure, Olivia Pérol, Rémi Ratajczak, Département cancer environnement (Centre Léon Bérard - Lyon), Centre Léon Bérard [Lyon], Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), 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-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), Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), International Agency for Research on Cancer (IARC), Ratajczak, Rémi, 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 Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
medicine.medical_specialty, Epidemiology, Health, Toxicology and Mutagenesis, 010501 environmental sciences, 01 natural sciences, 03 medical and health sciences, 0302 clinical medicine, Software, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], medicine, [SDV.EE.SANT] Life Sciences [q-bio]/Ecology, environment/Health, 030212 general & internal medicine, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, Global and Planetary Change, Land use, business.industry, Environmental resource management, Public Health, Environmental and Occupational Health, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 15. Life on land, Pollution, Geography, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], business
Abstract

International audience

Published
2019

13. Development of a software based on automatic multi-temporal aerial images classification to assess retrospective environmental exposures to pesticides in epidemiological studies

Author

Olivia Pérol, Laure Tougne, Elodie Faure, Béatrice Fervers, Rémi Ratajczak, Carlos Crispim-Junior, Centre Léon Bérard [Lyon], Extraction de Caractéristiques et Identification (imagine), 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), Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), Centre Léon Bérard, ADEME (N TEZ17-42), TESTIS, Département cancer environnement (Centre Léon Bérard - Lyon), Plateforme interface Santé-Environnement en Rhône-Alpes (EnvitéRA)Agence de l'environnement et de la maîtrise de l'énergie (ADEME), and Ratajczak, Rémi

Subjects
Crop acreage, Geographic information system, Epidemiology, [SHS.GEO] Humanities and Social Sciences/Geography, [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], [SDV.CAN]Life Sciences [q-bio]/Cancer, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], 010501 environmental sciences, Agricultural pesticides, 01 natural sciences, 03 medical and health sciences, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, Software, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.EE.SANT] Life Sciences [q-bio]/Ecology, environment/Health, 030212 general & internal medicine, 0105 earth and related environmental sciences, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, Land use, business.industry, Public Health, Environmental and Occupational Health, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [SHS.GEO]Humanities and Social Sciences/Geography, Environmental exposure, 15. Life on land, Pesticide, [SDE.ES]Environmental Sciences/Environmental and Society, Long latency, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Environmental science, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDE.ES] Environmental Sciences/Environmental and Society, business, Cartography
Abstract

International audience; Environmental exposure to agricultural pesticides (EEAP) resulting from the drift of agricultural pesticides from treated farmland is suspected to be a risk factor for several diseases, including cancers. The long latency period of cancer development, and evidence on the impact of early exposures stress the need for historical exposure information to capture these exposures. Geographic Information Systems (GIS) are increasingly used in environmental epidemiology studies to assess EEAP. Crop acreage proximate to subjects residences has been suggested as a surrogate for EEAP. Retrospective characterization of EEAP is then essential. While Corine Land Cover (CLC) provides land cover data since 1990, earlier data are lacking limiting the capacity to capture the life-course effects of exposure. The use of satellite images or historical aerial images has been suggested. However, characterization of land use from theses images is time and resource consuming. Thereby, this study aim to develop an innovative automated software to analyze the historical monochromatic aerial images in order to reconstruct the historical land cover to characterize EEAP retrospectively

Published
2018

14. A Fast Audiovisual Attention Model for Human Detection and Localization on a Companion Robot

Author

Rémi Ratajczak, denis pellerin, Quentin Labourey, Catherine Garbay, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA ), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), IARIA, ATTENTIVE, and ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)

Subjects
audiovisual attention, human localization, saliency, RGB-D, companion robot, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This paper describes a fast audiovisual attention model applied to human detection and localization on a companion robot. Its originality lies in combining static and dynamic modalities over two analysis paths in order to guide the robot's gaze towards the most probable human beings' locations based on the concept of saliency. Visual, depth and audio data are acquired using a RGB-D camera and two horizontal microphones. Adapted state-of-the-art methods are used to extract relevant information and fuse them together via two dimensional gaussian representations. The obtained saliency map represents human positions as the most salient areas. Experiments have shown that the proposed model can provide a mean F-measure of 66 percent with a mean precision of 77 percent for human localization using bounding box areas on 10 manually annotated videos. The corresponding algorithm is able to process 70 frames per second on the robot.

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