A Discrete Model for Color Naming

The ability to associate labels to colors is very natural for human beings. Though, this apparently simple task hides very complex and still unsolved problems, spreading over many different disciplines ranging from neurophysiology to psychology and imaging. In this paper, we propose a discrete model for computational color categorization and naming. Starting from the 424 color specimens of the OSA-UCS set, we propose a fuzzy partitioning of the color space. Each of the 11 basic color categories identified by Berlin and Kay is modeled as a fuzzy set whose membership function is implicitly defined by fitting the model to the results of an ad hoc psychophysical experiment (Experiment 1). Each OSA-UCS sample is represented by a feature vector whose components are the memberships to the different categories. The discrete model consists of a three-dimensional Delaunay triangulation of the CIELAB color space which associates each OSA-UCS sample to a vertex of a 3D tetrahedron. Linear interpolation is used to estimate the membership values of any other point in the color space. Model validation is performed both directly, through the comparison of the predicted membership values to the subjective counterparts, as evaluated via another psychophysical test (Experiment 2), and indirectly, through the investigation of its exploitability for image segmentation. The model has proved to be successful in both cases, providing an estimation of the membership values in good agreement with the subjective measures as well as a semantically meaningful color-based segmentation map

Alien Hand, Restless Brain: Salience Network and Interhemispheric Connectivity Disruption Parallel Emergence and Extinction of Diagonistic Dyspraxia

International audienceDiagonistic dyspraxia (DD) is by far the most spectacular manifestation reported by sufferers of acute corpus callosum (CC) injury (so-called "split-brain"). In this form of alien hand syndrome, one hand acts at cross purposes with the other "against the patient's will". Although recent models view DD as a disorder of motor control, there is still little information regarding its neural underpinnings, due to widespread connectivity changes produced by CC insult, and the obstacle that non-volitional movements represent for task-based functional neuroimaging studies. Here, we studied patient AM, the first report of DD in patient with complete developmental CC agenesis. This unique case also offers the opportunity to study the resting-state connectomics of DD in the absence of diffuse changes subsequent to CC injury or surgery. AM developed DD following status epilepticus (SE) which resolved over a 2-year period. Whole brain functional connectivity (FC) was compared (Crawford-Howell [CH]) to 16 controls during the period of acute DD symptoms (Time 1) and after remission (Time 2). Whole brain graph theoretical models were also constructed and topological efficiency examined. At Time 1, disrupted FC was observed in inter-hemispheric and intra-hemispheric right edges, involving frontal superior and midline structures. Graph analysis indicated disruption of the efficiency of salience and right frontoparietal (FP) networks. At Time 2, after remission of diagnostic dyspraxia symptoms, FC and salience network changes had resolved. In sum, longitudinal analysis of connectivity in AM indicates that DD behaviors could result from disruption of systems that support the experience and control of volitional movements and the ability to generate appropriate behavioral responses to salient stimuli. This also raises the possibility that changes to large-scale functional architecture revealed by resting-state functional magnetic resonance imaging (fMRI) (rs-fMRI) may provide relevant information on the evolution of behavioral syndromes in addition to that provided by structural and task-based functional imaging

Mise en correspondance d'images et de modèles (application à la reconstruction 3D de scènes sportives)

Cette thèse s inscrit dans le cadre du projet SimulFoot dont l objectif est l analyse de scènes de football. Nos problématiques concernent l analyse de séquences d images, la modélisation géométrique et la visualisation, ainsi que la simulation et l ergonomie cognitive. Nos principales recherches se sont focalisées sur des processus de traitement d images pour la reconstruction automatique d un modèle 3D d une scène à partir d une seule image. Une première étape permet la détection de la région d intérêt (le terrain) par une analyse colorimétrique. Nous illustrons notre méthodologie à travers l étude de la distribution des pixels dans l espace HLS. En complément, nous proposons une nouvelle façon de structurer, au niveau perceptuel, l espace des couleurs CIELab. Une seconde étape propose une approche originale d extraction (par transformées de Hough) des amers de la région d intérêt (droites et ellipses) et leur mise en correspondance avec des éléments remarquables du modèle 3D.This PhD is part of the SimulFoot project, whose main goal is the analysis of soccer scenes. Our research deals with image sequence analysis, geometrical modeling, and visualization, as well as simulation and cognitive studies. Our work focuses on image processing methods for automatically reconstructing a 3D model from a single image in the scene. A first step provides the detection of a region of interest (the field) by colorimetric analysis. Our method is illustrated with the study of pixel distribution in the HLS color space. We then propose a novel method for color space categorization at the perceptual level in CIELab space. A second step proposes an original approach for extracting (using a Hough transform) the landmarks of a region of interest (straight lines and ellipses) and matching them with the corresponding features in the 3D model.AIX-MARSEILLE2-BU Sci.Luminy (130552106) / SudocSudocFranceF

Soccer field detection in video images using color and spatial coherence

International audienceWe present an original approach based on the joint use of color and spatial coherence to automatically detect the soccer field in video sequences. We assume that the corresponding area is significant enough for that. This assumption is verified when the camera is oriented toward the field and does not focus on a given element of the scene such as a player or the ball. We do not have any assumption on the color of the field. We use this approach to automatically validate the image area in which the relevant scene elements are. This is a part of the SIMULFOOT project whose objective is the 3D reconstruction of the scene (players, referees, ball) and its animation as a support for cognitive studies and strategy analysis

Automatic selection of a region of interest in 3D scene images: application to video captured scenes

International audienceThis paper introduces an original approach to automatically select a Region of Interest in an image that represents a 3D scene. We assume that the Region of Interest background is significant enough to be characterized by its color and its spatial coherence. We use these two features to provide such a selection that is the first step of a 2D to 3D registration process for analyzing video captured sport scenes. The whole project includes the 3D reconstruction of the scene (players, referees, ball) and its animation as a support for cognitive studies and strategy analysis

Areas of muscle tissue alteration can differ from activated regions during electrically-induced isometric contractions

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Areas of muscle tissue alteration can differ from activated regions during electrically-induced isometric contractions

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Automatic Landmark Detection and Validation in Soccer Video Sequences

International audienceLandmarks are specific points that can be identified to provide efficient matching processes. Many works have been developed for detecting automatically such landmarks in images: our purpose is not to propose a new approach for such a detection but to validate the detected landmarks in a given context that is the 2D to 3D registration of soccer video sequences. The originality of our approach is that it globally takes into consideration the color and the spatial coherence of the field to provide such a validation. This process is a part of the SIMULFOOT project whose objective is the 3D reconstruction of the scene (players, referees, ball) and its animation as a support for cognitive studies and strategy analysis