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1. Segmenting the motion components of a video: A long-term unsupervised model

Author

Meunier, Etienne and Bouthemy, Patrick

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Human beings have the ability to continuously analyze a video and immediately extract the motion components. We want to adopt this paradigm to provide a coherent and stable motion segmentation over the video sequence. In this perspective, we propose a novel long-term spatio-temporal model operating in a totally unsupervised way. It takes as input the volume of consecutive optical flow (OF) fields, and delivers a volume of segments of coherent motion over the video. More specifically, we have designed a transformer-based network, where we leverage a mathematically well-founded framework, the Evidence Lower Bound (ELBO), to derive the loss function. The loss function combines a flow reconstruction term involving spatio-temporal parametric motion models combining, in a novel way, polynomial (quadratic) motion models for the spatial dimensions and B-splines for the time dimension of the video sequence, and a regularization term enforcing temporal consistency on the segments. We report experiments on four VOS benchmarks, demonstrating competitive quantitative results, while performing motion segmentation on a whole sequence in one go. We also highlight through visual results the key contributions on temporal consistency brought by our method.

Published
2023

2. EM-driven unsupervised learning for efficient motion segmentation

Author

Meunier, Etienne, Badoual, Anaïs, and Bouthemy, Patrick

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we present a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion models. The core idea of our work is to leverage the Expectation-Maximization (EM) framework in order to design in a well-founded manner a loss function and a training procedure of our motion segmentation neural network that does not require either ground-truth or manual annotation. However, in contrast to the classical iterative EM, once the network is trained, we can provide a segmentation for any unseen optical flow field in a single inference step and without estimating any motion models. We investigate different loss functions including robust ones and propose a novel efficient data augmentation technique on the optical flow field, applicable to any network taking optical flow as input. In addition, our method is able by design to segment multiple motions. Our motion segmentation network was tested on four benchmarks, DAVIS2016, SegTrackV2, FBMS59, and MoCA, and performed very well, while being fast at test time., Comment: Accepted to : IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Published
2022
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3. Learning how to be robust: Deep polynomial regression

Author

Perez-Rua, Juan-Manuel, Crivelli, Tomas, Bouthemy, Patrick, and Perez, Patrick

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods., Comment: 18 pages, conference

Published
2018

4. Tubelets: Unsupervised action proposals from spatiotemporal super-voxels

Author

Jain, Mihir, van Gemert, Jan, Jégou, Hervé, Bouthemy, Patrick, and Snoek, Cees G. M.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper considers the problem of localizing actions in videos as a sequences of bounding boxes. The objective is to generate action proposals that are likely to include the action of interest, ideally achieving high recall with few proposals. Our contributions are threefold. First, inspired by selective search for object proposals, we introduce an approach to generate action proposals from spatiotemporal super-voxels in an unsupervised manner, we call them Tubelets. Second, along with the static features from individual frames our approach advantageously exploits motion. We introduce independent motion evidence as a feature to characterize how the action deviates from the background and explicitly incorporate such motion information in various stages of the proposal generation. Finally, we introduce spatiotemporal refinement of Tubelets, for more precise localization of actions, and pruning to keep the number of Tubelets limited. We demonstrate the suitability of our approach by extensive experiments for action proposal quality and action localization on three public datasets: UCF Sports, MSR-II and UCF101. For action proposal quality, our unsupervised proposals beat all other existing approaches on the three datasets. For action localization, we show top performance on both the trimmed videos of UCF Sports and UCF101 as well as the untrimmed videos of MSR-II., Comment: submitted to International Journal of Computer Vision

Published
2016

5. Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow

Author

Fortun, Denis, Bouthemy, Patrick, and Kervrann, Charles

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Handling all together large displacements, motion details and occlusions remains an open issue for reliable computation of optical flow in a video sequence. We propose a two-step aggregation paradigm to address this problem. The idea is to supply local motion candidates at every pixel in a first step, and then to combine them to determine the global optical flow field in a second step. We exploit local parametric estimations combined with patch correspondences and we experimentally demonstrate that they are sufficient to produce highly accurate motion candidates. The aggregation step is designed as the discrete optimization of a global regularized energy. The occlusion map is estimated jointly with the flow field throughout the two steps. We propose a generic exemplar-based approach for occlusion filling with motion vectors. We achieve state-of-the-art results in computer vision benchmarks, with particularly significant improvements in the case of large displacements and occlusions., Comment: Submission,IEEE Transactions on Image Processing (2014)

Published
2014

10. Apprentissage profond non supervisé fondé sur l'algorithme EM pour la segmentation du mouvement

Author

Meunier, Etienne, Badoual, Anaïs, Bouthemy, Patrick, Meunier, Etienne, Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes (SERPICO), Inria Rennes – Bretagne Atlantique, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
optical flow, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], flot optique, algorithme EM, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Segmentation du mouvement, Motion segmentation, EM algorithm, CNN, réseau de neurones
Abstract

This paper presents a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion models. The core idea of this work is to leverage the Expectation-Maximization (EM) framework. It enables us to design in a well-founded manner the loss function and the training procedure of our motion segmentation neural network. However, in contrast to the classical iterative EM, once the network is trained, we can provide a segmentation for any unseen optical flow field in a single inference step, with no dependence on the initialization of the motion model parameters since they are not estimated in the inference stage. Our method outperforms comparable unsupervised methods and is very efficient., Cet article présente une méthode entièrement non supervisée basée sur un réseau neuronal convolutionnel pour la segmentation du mouvement à partir du flot optique. Nous supposons que le flot optique d'entrée peut être représenté par un ensemble de modèles de mouvement paramétriques, typiquement des modèles de mouvement affines ou quadratiques. L'idée centrale de ce travail est d'exploiter le cadre de l'Espérance-Maximisation (EM). Il nous permet de concevoir d'une manière bien fondée la fonction de perte et la procédure d'apprentissage de notre réseau de neurones de segmentation du mouvement. Contrairement à la méthode EM itérative classique, une fois le réseau entraîné, nous pouvons fournir une segmentation pour tout nouveau champ de flot optique en une seule étape d'inférence et sans avoir avoir à estimer de modèles de mouvement. Notre méthode surpasse les méthodes non supervisées comparables et est très efficace en temps de calcul. Mots Clés Segmentation du mouvement, flot optique, réseau de neurones, algorithme EM.

Published
2022

12. EM-Driven Unsupervised Learning for Efficient Motion Segmentation

Author

Meunier, Etienne, Badoual, Anais, and Bouthemy, Patrick

Abstract

In this paper, we present a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion models. The core idea of our work is to leverage the Expectation-Maximization (EM) framework in order to design in a well-founded manner a loss function and a training procedure of our motion segmentation neural network that does not require either ground-truth or manual annotation. However, in contrast to the classical iterative EM, once the network is trained, we can provide a segmentation for any unseen optical flow field in a single inference step and without estimating any motion models. We investigate different loss functions including robust ones and propose a novel efficient data augmentation technique on the optical flow field, applicable to any network taking optical flow as input. In addition, our method is able by design to segment multiple motions. Our motion segmentation network was tested on four benchmarks, DAVIS2016, SegTrackV2, FBMS59, and MoCA, and performed very well, while being fast at test time.

Published
2023
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15. A statistical video content recognition method using invariant features on object trajectories

Author

Hervieu, Alexandre, Bouthemy, Patrick, and Le Cadre, Jean-Pierre

Subjects
Markov processes -- Evaluation, Motion perception (Vision) -- Technology application, Object recognition (Computers) -- Evaluation, Pattern recognition -- Evaluation, Signal processing -- Methods, Automatic classification -- Methods, Video equipment -- Research, Technology application, Digital signal processor, Business, Computers, Electronics, Electronics and electrical industries
Abstract

This work is dedicated to a statistical trajectory-based approach addressing two issues related to dynamic video content understanding: recognition of events and detection of unexpected events. Appropriate local differential features combining curvature and motion magnitude are defined and robustly computed on the motion trajectories in the image sequence. These features are invariant to image translation, in-the-plane rotation and spatial scaling. The temporal causality of the features is then captured by Hidden Markov Models dedicated to trajectory description, whose states are properly quantized values. The similarity between trajectories is expressed by exploiting this quantization-based HMM framework. Moreover statistical techniques have been developed for parameter estimations. Evaluations of the method have been conducted on several data sets including real trajectories obtained from sport videos, especially Formula One and ski TV program. The novel method compares favorably with other methods including feature histogram comparisons, HMM/GMM modeling and SVM classification. Index Terms--Hidden Markov models, motion analysis, pattern classification, video signal processing.

Published
2008

17. Space-time adaptation for patch-based image sequence restoration

Author

Boulanger, Jerome, Kervrann, Charles, and Bouthemy, Patrick

Subjects
Image processing -- Analysis, Nonparametric statistics -- Usage, Nonparametric statistics -- Analysis
Abstract

We present a novel space-time patch-based method for image sequence restoration. We propose an adaptive statistical estimation framework based on the local analysis of the bias-variance trade-off. At each pixel, the space-time neighborhood is adapted to improve the performance of the proposed patch-based estimator. The proposed method is unsupervised and requires no motion estimation. Nevertheless, it can also be combined with motion estimation to cope with very large displacements due to camera motion. Experiments show that this method is able to drastically improve the quality of highly corrupted image sequences. Quantitative evaluations on standard artificially noise-corrupted image sequences demonstrate that our method outperforms other recent competitive methods. We also report convincing results on real noisy image sequences. Index Terms--Image sequence restoration, denoising, nonparametric estimation, nonlinear filtering, bias-variance trade-off.

Published
2007

19. Recognition of dynamic video contents with global probabilistic models of visual motion

Author

Piriou, Gwenaelle, Bouthemy, Patrick, and Jian-Feng Yao

Subjects
Image coding -- Analysis, Image processing -- Analysis, Probabilities -- Analysis, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The problem of recognizing dynamic video contents from low-level motion features is addressed by adopting a statistical approach involving modeling, supervised learning and classification issues. Parsimonious global probabilistic visual motion models are proposed for the dominant image motion and the residual image motion, while the dynamic content recognition results on sports videos are presented to validate the proposed motion modeling and recognition framework.

Published
2006

20. Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval

Author

Fablet, Ronan, Bouthemy, Patrick, and Perez, Patrick

Subjects
Image processing -- Research, Video recordings -- Research, Video recording industry -- Research, Information storage and retrieval systems -- Research, Computer software industry -- Research, Imaging technology, DBMS, Business, Computers, Electronics, Electronics and electrical industries
Abstract

A new approach for content-based video indexing and retrieval is presented. Statistical modeling produces a general statistical framework for motion-based hierarchical classification of a video database and motion-based retrieval with query-by-example according to the maximum a posteriori (MAP) criterion.

Published
2002

21. Unsupervised segmentation of low clouds from infrared METEOSAT images based on a contextual spatio-temporal labeling approach

Author

Papin, Christophe, Bouthemy, Patrick, and Rochard, Guy

Subjects
Earth sciences -- Research, Clouds -- Photographs from space, Business, Earth sciences, Electronics and electrical industries
Abstract

The early and accurate segmentation of low clouds during the night-time is an important task for nowcasting. It requires that observations can be acquired at a sufficient time rate as provided by the geostationary METEOSAT satellite over Europe. However, the information supplied by the single infrared METEOSAT channel available by night is not sufficient to discriminate between low clouds and ground during night from a single image. To tackle this issue, we consider several sources of information extracted from an infrared image sequence. Indeed, we exploit both relevant local motion-based measurements, intensity images and thermal parameters estimated over blocks, along with local contextual information. A statistical contextual labeling process in two classes, involving 'low clouds' and 'clear sky,' is performed on the warmer pixels. It is formulated within a Bayesian estimation framework associated with Markov random field (MRF) models. This comes to minimize a global energy function comprising three terms: two data-driven terms (thermal and motion-based ones) and a regularization term expressing a priori knowledge on the label field (expected spatial contextual properties). We propose a progressive minimization procedure of this energy function starting from initial reliably labeled pixels and involving only local computation. Thermal parameters associated to each class are estimated according to an unsupervised learning scheme enabling the handling of spatiotemporal nonstationarities. Our method produces segmentation maps displaying temporal coherency along the image sequence. Experimental results on representative meteorological situations are reported and favorably compared with NOAA/AVHRR cloud classifications which serve as reference results. They demonstrate the accuracy and efficiency of the proposed approach. Index Terms--Contextual labeling, low clouds detection, Markov random field (MRF) models, METEOSAT satellite images, spatio-temporal image segmentation, thermal parameters estimation.

Published
2002

24. A unified approach to shot change detection and camera motion characterization

Author

Bouthemy, Patrick, Gelgon, Marc, and Ganansia, Fabrice

Subjects
Image transmission -- Research, Video cameras -- Usage, Motion -- Analysis, Business, Computers, Electronics, Electronics and electrical industries
Abstract

This paper describes an original approach to partitioning of a video document into shots. Instead of an interframe similarity measure which is directly intensity based, we exploit image motion information, which is generally more intrinsic to the video structure itself. The proposed scheme aims at detecting all types of transitions between shots using a single technique and the same parameter set, rather than a set of dedicated methods. The proposed shot change detection method is related to the computation, at each time instant, of the dominant image motion represented by a two-dimensional affine model. More precisely, we analyze the temporal evolution of the size of the support associated to the estimated dominant motion. Besides, the computation of the global motion model supplies by-products, such as qualitative camera motion description, which we describe in this paper, and other possible extensions, such as mosaicing and mobile zone detection. Results on videos of various content types are reported and validate the proposed approach.

Published
1999

27. Structure from controlled motion

Author

Chaumette, Francois, Boukir, Samia, Bouthemy, Patrick, and Juvin, Didier

Subjects
Machine vision -- Research, Incremental motion control -- Research
Abstract

This paper deals with the recovery of 3D information using a single mobile camera in the context of active vision. First, we propose a general revisited formulation of the structure-from-known-motion issue. Within the same formalism, we handle various kinds of 3D geometrical primitives such as points, lines, cylinders, spheres, etc. We also aim at minimizing effects of the different measurement errors which are involved in such a process. More precisely, we mathematically determine optimal camera configurations and motions which lead to a robust and accurate estimation of the 3D structure parameters. We apply the visual servoing approach to perform these camera motions using a control law in closed-loop with respect to visual data. Real-time experiments dealing with 3D structure estimation of points and cylinders are reported. They demonstrate that this active vision strategy can very significantly improve the estimation accuracy. Index Terms - Computer vision, robotics, active vision, structure from motion, vision-based control.

Published
1996

28. Multimodal estimation of discontinuous optical flow using Markov random fields

Author

Heitz, Fabrice and Bouthemy, Patrick

Subjects
Markov processes -- Usage, Optical data processing -- Research, Image processing -- Research
Abstract

The estimation of dense velocity fields from image sequences is basically an ill-posed problem, primarily because the data only partially constrain the solution. It is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches. In this paper, we present a multimodal approach to the problem of motion estimation in which the computation of visual motion is based on several complementary constraints. It is shown that multiple constraints can provide more accurate flow estimation in a wide range of circumstances. The theoretical framework relies on bayesian estimation associated with global statistical models, namely, Markov Random Fields. The constraints introduced here aim to address the following issues: optical flow estimation while preserving motion boundaries, processing of occlusion regions, fusion between gradient and feature-based motion constraint equations. Deterministic relaxation algorithms are used to merge information and to provide a solution to the maximum a posteriori estimation of the unknown dense motion field. The algorithm is well suited to a multiresolution implementation which brings an appreciable speed-up as well as a significant improvement of estimation when large displacements are present in the scene. Experiments on synthetic and real world image sequences are reported.

Published
1993

29. Detection and Localization of Anomalous Motion in Video Sequences from Local Histograms of Labeled Affine Flows

Author

Pérez-Rúa, Juan-Manuel, Basset, Antoine, Bouthemy, Patrick, Technicolor [Cesson Sévigné], Technicolor, Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes (SERPICO), Inria Rennes – Bretagne Atlantique, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
ICT, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], motion patterns, anomalous motion detection, lcsh:Electronic computers. Computer science, affine flow, video processing, local outlier factor, lcsh:QA75.5-76.95
Abstract

International audience; We propose an original method for detecting and localizing anomalous motion patterns in videos from a camera view-based motion representation perspective. Anomalous motion should be taken in a broad sense, i.e., unexpected, abnormal, singular, irregular, or unusual motion. Identifying distinctive dynamic information at any time point and at any image location in a sequence of images is a key requirement in many situations and applications. The proposed method relies on so-called labeled affine flows (LAF) involving both affine velocity vectors and affine motion classes. At every pixel, a motion class is inferred from the affine motion model selected in a set of candidate models estimated over a collection of windows. Then, the image is subdivided in blocks where motion class histograms weighted by the affine motion vector magnitudes are computed. They are compared blockwise to histograms of normal behaviors with a dedicated distance. More specifically, we introduce the local outlier factor (LOF) to detect anomalous blocks. LOF is a local flexible measure of the relative density of data points in a feature space, here the space of LAF histograms. By thresholding the LOF value, we can detect an anomalous motion pattern in any block at any time instant of the video sequence. The threshold value is automatically set in each block by means of statistical arguments. We report comparative experiments on several real video datasets, demonstrating that our method is highly competitive for the intricate task of detecting different types of anomalous motion in videos. Specifically, we obtain very competitive results on all the tested datasets: 99.2% AUC for UMN, 82.8% AUC for UCSD, and 95.73% accuracy for PETS 2009, at the frame level.

Published
2017

30. 3D flow field estimation and assessment for live cell fluorescence microscopy.

Author

Manandhar, Sandeep, Bouthemy, Patrick, Welf, Erik, Danuser, Gaudenz, Roudot, Philippe, and Kervrann, Charles

Subjects
*FLUORESCENCE microscopy, *SINGLE molecules, *EYE, *MICROSCOPY, *IMAGE processing, *PIPELINES
Abstract

Motivation The revolution in light sheet microscopy enables the concurrent observation of thousands of dynamic processes, from single molecules to cellular organelles, with high spatiotemporal resolution. However, challenges in the interpretation of multidimensional data requires the fully automatic measurement of those motions to link local processes to cellular functions. This includes the design and the implementation of image processing pipelines able to deal with diverse motion types, and 3D visualization tools adapted to the human visual system. Results Here, we describe a new method for 3D motion estimation that addresses the aforementioned issues. We integrate 3D matching and variational approach to handle a diverse range of motion without any prior on the shape of moving objects. We compare different similarity measures to cope with intensity ambiguities and demonstrate the effectiveness of the Census signature for both stages. Additionally, we present two intuitive visualization approaches to adapt complex 3D measures into an interpretable 2D view, and a novel way to assess the quality of flow estimates in absence of ground truth. Availability and implementation https://team.inria.fr/serpico/data/3d-optical-flow-data/ Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2020
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31. Modélisation et estimation de la diffusion de protéines à l’exocytose dans des séquences d’images de microscopie TIRF

Author

Basset, Antoine, Bouthemy, Patrick, Boulanger, Jérôme, Waharte, François, Salamero, Jean, Kervrann, Charles, Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes (SERPICO), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Mécanismes moléculaires du transport intracellulaire, Compartimentation et dynamique cellulaires (CDC), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC), BioImaging Cell and Tissue Core Facility (PICT-IBiSA), Institut Curie [Paris], Région Bretagne, Association Française de Reconnaissance et d'Interprétation des Formes (AFRIF), Association Française d'Intelligence Artificielle (AFIA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), and Kervrann, Charles

Subjects
Séquence d’images, estimation de la diffusion, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], exocytose, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], microscopie TIRF, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, [SDV.BC] Life Sciences [q-bio]/Cellular Biology
Abstract

National audience; Dans le contexte de l’analyse de la dynamique membra-naire, nous proposons un nouveau modèle pour représen-ter le comportement de protéines au cours de la fusion devésicules dans la membrane plasmique à la fin du proces-sus d’exocytose. Le modèle introduit incorpore la phase delibération continue des protéines de la vésicule, caracté-risée par le taux de libération. Les protéines diffusent en-suite dans la membrane, ce qui ajoute un second paramètrebiophysique, le coefficient de diffusion. Une méthode origi-nale est élaborée pour estimer ces deux paramètres à par-tir d’une séquence d’images. Une évaluation quantitativeen démontre l’efficacité, et les résultats sur des séquencesréelles de microscopie TIRF permettent de mettre en évi-dence des différences de comportement des protéines Lan-gerin et TfR.

Published
2016

33. Real-Time Tracking of Moving Persons by Exploiting Spatio-Temporal Image Slices

Author

Ricquebourg, Yann and Bouthemy, Patrick

Subjects
Electronic surveillance -- Testing, Kalman filtering -- Testing, Motion -- Testing
Abstract

This paper addresses the problem of analyzing human motion in an image sequence. This is of particular importance for video-surveillance applications. We have developed an original and efficient approach to track the apparent contours of a moving articulated structure, avoiding the use of 3D models. This method exploits spatio-temporal XT-slices from the image sequence volume XYT, where typical trajectory patterns can be associated with articulated motion such as human walking. We reconstruct these trajectories online by introducing an appropriate predictive model while correctly handling occlusion periods. This paradigm can lead to a simple trajectory recognition scheme. Experiments with real-world images depicting human walk or gesture are reported, and obtained results validate the proposed approach. Index Terms--Image sequence, tracking and prediction, human motion analysis, video surveillance, contour displacement, Kalman filtering.

Published
2000

34. Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow Shapes in Sonar Imagery

Author

Mignotte, Max, Collet, Christophe, Perez, Patrick, and Bouthemy, Patrick

Subjects
Sonar systems -- Design and construction, Classification -- Models
Abstract

We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process. First, the detection problem of a region of interest in the input image is stated as the minimization of a cost function. Second, the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique (a gradient-based algorithm) and two stochastic relaxation methods: Simulated Annealing (SA) and a hybrid Genetic Algorithm (GA). This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results. Index Terms--Deformable template, objective function, simulated annealing, gradient-based algorithm, genetic optimization, shape recognition, sonar imagery.

Published
2000

35. Sélection d'échelle automatique précise et seuillage localement adapté pour la segmentation de vésicules en microscopie TIRF

Author

Basset, Antoine, Boulanger, Jérôme, Bouthemy, Patrick, Kervrann, Charles, Salamero, Jean, Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes (SERPICO), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Mécanismes moléculaires du transport intracellulaire, Compartimentation et dynamique cellulaires (CDC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), Vision spatio-temporelle et active (VISTA), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC), and Adam, Sébastien

Subjects
[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], segmentation, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Microscopie TIRF, sélection d'échelle
Abstract

National audience; En imagerie cellulaire et microscopie de fluorescence, la première phase de l'analyse consiste généralement à détecter une grande quantité d'éléments (molécules, vésicules). De nombreuses méthodes de détection ont été développées dans ce contexte, mais elles réclament souvent un paramétrage assez fin et ne peuvent pas toujours s'adapter à l'échelle des éléments à détecter. Nous proposons une méthode incluant une sélection d'échelle automatique précise et un seuillage localement adapté. Des expérimentations quantitatives sur images simulées montrent les avantages et les meilleures performances de notre méthode. Des résultats sur séquences réelles confirment son intérêt.

Published
2014

39. Network Tomography-based tracking for intracellular traffic analysis in fluorescence microscopy imaging

Author

Pecot, Thierry, Kervrann, Charles, Bouthemy, Patrick, Unité de biométrie et intelligence artificielle de Jouy (MIA-JOUY), Institut National de la Recherche Agronomique (INRA), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Unité de biométrie et intelligence artificielle de jouy, CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)

Subjects
microscopie, trafficking, membrane transport, [INFO]Computer Science [cs], object tracking, fluorescence microscopy, network tomography, Voronoi diagram, [MATH]Mathematics [math]
Abstract

International audience; Determination of the sub-cellular localization and dynamics of any proteins is an important step towards the understanding of multi-molecular complexes in a cellular context. Green Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy allows to acquire multidimensional data on rapid cellular activities, and then make possible the analysis of proteins of interest. Consequently, novel techniques of image analysis are needed to quantify dynamics of biological processes observed in such image sequences. In biological trafficking analysis, the previous tracking methods do not manage when many small and poorly distinguishable objects interact. Nevertheless, an another way of tracking that usually consists in determining the full trajectories of all the objects, can be more relevant. General information about the traffic like the regions of origin and destination of the moving objects represent interesting features for analysis. In this paper, we propose to estimate the paths (regions of origin and destination) used by the objects of interest, and the proportions of moving objects for each path. This can be accomplished by exploiting the recent advances in Network Tomography (NT) commonly used in network communications. This idea is demonstrated on real image sequences for the Rab6 protein, a GTPase involved in the regulation of intracellular membrane trafficking.

Published
2008

40. Simulation and estimation of intra-cellular dynamics and trafficking

Author

Boulanger, J., Pecot, Thierry, Bouthemy, Patrick, Salamero, J., Sibarita, J.B., Kervrann, Charles, Biologie Cellulaire et Cancer, Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Unité de biométrie et intelligence artificielle de Jouy (MIA-JOUY), Institut National de la Recherche Agronomique (INRA), Université Pierre et Marie Curie - Paris 6 (UPMC), Vision spatio-temporelle et active (VISTA), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Compartimentation et dynamique cellulaires (CDC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Curie [Paris], Jens Rittscher (Editeur), T.C. Wong Stephen (Editeur), Raghu Machiraju (Editeur), Unité de biométrie et intelligence artificielle de jouy, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC)

Subjects
Fluorescence microscopy, vesicle trafficking, image analysis, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

Le CD-ROM contient des données iconographiques; International audience; In this chapter, we present an original modeling framework for membrane trafficking modeling and video-microscopy. Understanding the complexity of cellular processes requires a better knowledge of their dynamic properties and geometric structures. Over the last decade, progresses in cellular and molecular biology as well as in microscopy allow cell visualization while acquiring multidimensional data of their fastly occurring activities. The proposed method is devoted to the analysis of the dynamical content of microscopy image sequences corresponding to dynamical events related to membrane transport. This chapter is organized as follows. In Section 7.1, the major biological issues are introduced, as well as the optical instruments used for investigation. In this chapter, video-microscopy reveals the dynamical behavior of tagged proteins within the living cell. To capture the movements of these proteins, we propose new models to mimic trafficking of proteins within the cell by making the analogy with computer networks or road networks (Section 7.2). In addition, we provide a framework for simulating the foreground and background components observed in real image sequences. Section 7.3 is dedicated to the estimation of the background model. In Section 7.4, we consider the problem of estimating the origin-destination probabilities of the tagged proteins for the trafficking network

Published
2008

41. Network tomography for tra cking simulation and analysis in fluorescence microscopy imaging

Author

Pécot, Thierry, Boulanger, Jérôme, Kervrann, Charles, Bouthemy, Patrick, Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes (SERPICO), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Mécanismes moléculaires du transport intracellulaire, Compartimentation et dynamique cellulaires (CDC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), Pécot, Thierry, and Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC)

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [SPI.OPTI] Engineering Sciences [physics]/Optics / Photonic, [STAT.AP] Statistics [stat]/Applications [stat.AP], [SDV.BC.BC] Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC]
Abstract

International audience; GFP-tagging and time-lapse fluorescence microscopy can be considered as investigation tools to observe molecular dynamics and interactions in live cells at both the microscopic and nanoscopic scales. Consequently, it is imperative to develop novel image analysis techniques able to quantify dynamics of biological processes observed in such image sequences. This motivates our present research effort which is to develop novel methods to extract information from nD data. In trafficking analysis, object tracking using conventional techniques can be very hard or impossible, especially when more than one hundred small and poorly distinguishable objects interact. However, determining the full trajectories of all the objects are not needed to monitor the cell activity. Indeed, estimating origin and destination regions of the objects of interest may be more relevant. In this paper, we propose an original approach to recover the origin and destination pairs from traffic information. Thus, we propose to consider the membrane trafficking as a road trafficking, and for the first time we exploit the recent advances in Network Tomography (NT) commonly used in network communication for biological trafficking analysis. This idea is demonstrated on realistic artificial image sequences for the Rab6 protein, a GTPase involved in the regulation of intracellular membrane trafficking.

Published
2007

42. Tomographie de réseau appliquée à la simulation et à l’analyse de trafic dans des séquences d’images de microscopie à fluorescence

Author

Pecot, Thierry, Boulanger, Jérôme, Kervrann, Charles, Bouthemy, Patrick, Unité de biométrie et intelligence artificielle de jouy, Institut National de la Recherche Agronomique (INRA), Vision spatio-temporelle et active (VISTA), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Unité de biométrie et intelligence artificielle de Jouy (MIA-JOUY), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique

Subjects
analyse d'images, inference, microscopie, time-lapse microscopy, reseau, video-microscopie, trafic membranaire, network, membrane trafic, tracking, BIOLOGIE CELLULAIRE, statistique, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; La technique de marquage avec la protéine GFP “Green Fluorescent Protein” et la vidéomicroscopie à fluorescence sont des outils d’investigation permettant d’observer des dynamiques et des interactions moléculaires dans des cellules vivantes, tant à l’échelle microscopique qu’à l’échelle nanoscopique. Par conséquent, il est impératif de développer de nouvelles techniques d’analyse d’images capables de quantifier les dynamiques des processus biologiques observés dans ces séquences. Ceci motive notre effort de recherche qui consiste à développer de nouvelles méthodes d’extraction d’informations à partir de données nD. Dans l’analyse de trafic, le suivi d’objets s’appuyant sur des techniques conventionnelles peut s’avérer très complexe, voire impossible, surtout quand un grand nombre de petits objets coalescents sont en interaction. Néanmoins, l’estimation des trajectoires complètes de tous les objets n’est pas toujours nécessaire à la compréhension et la mesure de l’activité cellulaire. En effet, estimer les régions “origine” et “destination” de ces objets peut s’avérer plus pertinente. Dans cet article, nous proposons une approche originale pour inférer les zones “origine” et “destination” à partir d’informations partielles relatives au trafic. Ainsi, le trafic mem- branaire est assimilé à un trafic routier, ce qui permet alors d’exploiter les récentes avancées en Tomographie de Réseau (TR) bien connues dans la communauté réseaux de communication pour étudier le trafic vésiculaire. Cette approche est validée sur des séquences d’images artificielles relatives à la protéine Rab6, une GTPase impliquée dans la régulation du trafic membranaire intracellulaire.

Published
2007

43. Simulation and estimation of fluorescence microscopy image sequences for intracellular dynamics and trafficking

Author

Boulanger, Jérôme, Kervrann, Charles, Bouthemy, Patrick, Unité de biométrie et intelligence artificielle de jouy, Institut National de la Recherche Agronomique (INRA), Vision spatio-temporelle et active (VISTA), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Unité de biométrie et intelligence artificielle de Jouy (MIA-JOUY), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique

Subjects
microscopie, statistiques, time-lapse microscopy, trafic membranaire, modélisation spatiale, membrane traffic, réseau, image processing, traitement d'image, modèle mathématique, video-microscopie, statistics, [INFO]Computer Science [cs], [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2006

44. Adaptive space-time patch-based method for image sequence restoration

Author

Boulanger, Jérôme, Kervrann, Charles, Bouthemy, Patrick, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Vision spatio-temporelle et active (VISTA), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Unité de biométrie et intelligence artificielle de jouy, Institut National de la Recherche Agronomique (INRA), Université de Rennes (UR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and Unité de biométrie et intelligence artificielle de Jouy (MIA-JOUY)

Subjects
evaluation nonparamétrique, bias-variance trade-off, removal, diffusion, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, mouvement, INTELLIGENCE ARTIFICIELLE, nonparametric estimation, image restauration d'ordre, nonlinear filtering, INFORMATIQUE, motion, filtrage non-lineaire, image sequence restoration, deplacement, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; We present a novel space-time exemplar-based method for image sequence restoration. We have designed a locally adaptive estimation framework which relies on the analysis of the bias-variance trade-off. It allows us to select on-line the best adapted filtering space-time neigh-borhood at each pixel. Furthermore, it involves an original space-time exemplar-based restoration which improves the overall performance. This method is unsupervised and requires no motion estimation. It nevertheless can be combined with motion estimation to cope with very large displacements due to camera motion. Experiments show that this method is able to drastically improve the quality of highly corrupted image sequences. Quantitative results on standard image sequences demonstrate that our approach outperforms other recent methods.

Published
2006

45. Automatic morphological detection of otolith nucleus

Author

Cao, Frédéric, Bouthemy, Patrick, Fablet, Ronan, Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Département Signal et Communications (SC), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), and Télécom Bretagne (devenu IMT Atlantique), Ex-Bibliothèque

Subjects
0106 biological sciences, Computer science, 02 engineering and technology, Mathematical morphology, 01 natural sciences, Set (abstract data type), A contrario detection, Artificial Intelligence, Component (UML), 0202 electrical engineering, electronic engineering, information engineering, medicine, Segmentation, Computer vision, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Otolith, Basis (linear algebra), business.industry, 010604 marine biology & hydrobiology, Otolith imaging, medicine.anatomical_structure, Signal Processing, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software
Abstract

This paper deals with the automation of the acquisition of age and growth data from the analysis of otolith images. One of the key component of such it system is the automatic detection of the growth center, which serves as the basis for further processing (2D ring segmentation, age and growth estimation,...). To this end, we develop a new and well-founded approach combining a morphological analysis of image level sets to the a contrario, detection principle. This approach leads to a robust parameterless scheme, whose efficiency is demonstrated by an evaluation carried out for a set of several hundred of plaice otoliths. (c) 2005 Elsevier B.V. All rights reserved.

Published
2006

46. Modélisation statistique non paramétrique et reconnaissance du mouvement

Author

Fablet, Ronan, Bouthemy, Patrick, Département Signal et Communications (SC), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), and Télécom Bretagne, Bibliothèque

Subjects
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

National audience; *

Published
2002

47. Statistical modeling for motion-based video classification and retrieval

Author

Fablet, Ronan, Bouthemy, Patrick, Département Signal et Communications (SC), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Télécom Bretagne, Bibliothèque

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; We have developed an original approach for content-based video indexing and retrieval. By introducing a causal Gibbsian modeling of the spatio-temporal distribution of appropriate local motion-related measurements, we have designed a general and efficient statistical framework for non parametric motion modeling, motion recognition and classification, and motion segmentation. It is exploited for motion-based video indexing and video retrieval for both global and partial queries by example.

Published
2001

48. Statistical motion-based video indexing and retrieval

Author

Fablet, Ronan, Bouthemy, Patrick, Pérez, Patrick, Département Signal et Communications (SC), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Télécom Bretagne, Bibliothèque

Subjects
Computer Science::Multimedia, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

International audience; We propose an original approach for the characterization of video dynamic content with a view to supplying new functionalities for motion-based video indexing and retrieval with query by example. We have designed a statistical framework for motion content description without any prior motion segmentation, and for motion-based video classification and retrieval. Contrary to other proposed methods, we do not extract from a given video sequence a set of motion features but we identify a global probabilistic model, expressed as a temporal Gibbs random field. This leads to define an efficient statistical motion-based similarity measure, relying on the computation of conditional likelihoods, to discriminate various motion contents. We have carried out experiments on a set of 100 video sequences, representative of various motion situations (temporal textures as fire and crowd motions, sport videos, car sequences, low motion activity examples)

Published
2000

49. Sonar image segmentation using an unsupervised hierarchical MRF model

Author

Mignotte, Max, Collet, Christophe, Perez, Patrick, and Bouthemy, Patrick

Subjects
Image processing -- Research, Markov processes -- Research, Sonar systems -- Research, Business, Computers, Electronics, Electronics and electrical industries
Abstract

Hierarchical Markov random field models applied to sonar image segmentation are examined.

Published
2000

50. Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences.

Author

Boulanger, Jérôme, Kervrann, Charles, Bouthemy, Patrick, Elbau, Peter, Sibarita, Jean-Baptiste, and Salamero, Jean

Subjects
THREE-dimensional imaging, FLUORESCENCE microscopy, SIGNAL-to-noise ratio, RANDOM noise theory, IMAGE stabilization, NOISE control, VIDEO microscopy, ALGORITHMS
Abstract

We present a nonparametric regression method for denoising 3-D image sequences acquired via fluorescence microscopy. The proposed method exploits the redundancy of the 3-D+time information to improve the signal-to-noise ratio of images corrupted by Poisson-Gaussian noise. A variance stabilization transform is first applied to the image-data to remove the dependence between the mean and variance of intensity values. This preprocessing requires the knowledge of parameters related to the acquisition system, also estimated in our approach. In a second step, we propose an original statistical patch-based framework for noise reduction and preservation of space-time discontinuities. In our study, discontinuities are related to small moving spots with high velocity observed in fluorescence video-microscopy. The idea is to minimize an objective nonlocal energy functional involving spatio-temporal image patches. The minimizer has a simple form and is defined as the weighted average of input data taken in spatially-varying neighborhoods. The size of each neighborhood is optimized to improve the performance of the pointwise estimator. The performance of the algorithm (which requires no motion estimation) is then evaluated on both synthetic and real image sequences using qualitative and quantitative criteria. [ABSTRACT FROM AUTHOR]

Published
2010
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