abstract: The present study explores the role of motion in the perception of form from dynamic occlusion, employing color to help isolate the contributions of both visual pathways. Although the cells that respond to color cues in the environment usually feed into the ventral stream, humans can perceive motion based on chromatic cues. The current study was designed to use grey, green, and red stimuli to successively limit the amount of information available to the dorsal stream pathway, while providing roughly equal information to the ventral system. Twenty-one participants identified shapes that were presented in grey, green, and red and were defined by dynamic occlusion. The shapes were then presented again in a static condition where the maximum occlusions were presented as before, but without motion. Results showed an interaction between the motion and static conditions in that when the speed of presentation increased, performance in the motion conditions became significantly less accurate than in the static conditions. The grey and green motion conditions crossed static performance at the same point, whereas the red motion condition crossed at a much slower speed. These data are consistent with a model of neural processing in which the main visual systems share information. Moreover, they support the notion that presenting stimuli in specific colors may help isolate perceptual pathways for scientific investigation. Given the potential for chromatic cues to target specific visual systems in the performance of dynamic object recognition, exploring these perceptual parameters may help our understanding of human visual processing. / Dissertation/Thesis / M.A. Psychology 2011

O termo neuroplasticidade se refere às mudanças funcionais ou anatômicas que ocorrem no sistema nervoso decorrentes de experiências. O enriquecimento ambiental (EA) é um dos modelos experimentais utilizados para estudar eventos relacionados à neuroplasticidade, pois aumenta a neurogênese, os níveis de neurotrofinas, a sobrevivência neuronal, a sinaptogênese, além de induzir cascatas de sinalização e uma mudança na arborização dendritica dos neurônios de diversas regiões encefálicas. Nos últimos anos a importância dos astrócitos tem ganho destaque, principalmente no que tange a sua capacidade de modular sinapses e na participação ativa em eventos plásticos no encéfalo, essas mudanças estão relacionadas a alterações funcionais e morfológicas. O EA é um modelo interessante para avaliar performances comportamentais pois é um modelo que ocasiona mudanças na capacidade de armazenar e acessar novas informações. Sendo assim, acreditamos que o EA é capaz de gerar efeitos plásticos nos astrócitos do Stratum Radiatum da região CA1 e alterar as respostas comportamentais a tarefa de reconhecimento de objetos. Após oito semanas de EA iniciado logo após o desmame, camundongos CF-1 albinos não apresentam diferenças significativas no número de astrócitos GFAP-ir e na densidade óptica para GFAP no Stratum Radiatum. Entretanto ocorreu um aumento no número de processos originários do soma, aumento no tamanho destes processos no eixo lateral, paralelo as colaterais de Schaffer e no número de intersecções aos círculos concêntricos de Scholl na mesma região. Os astrócitos adquirem um formato estrelado, este achado pode estar relacionado ao aumento da densidade sináptica nesta região e corroboram com a idéia de que modificações astrocitárias são parte ativa dos processos plásticos que ocorrem no encéfalo. Nossos resultados mostraram também uma mudança comportamental na resposta a tarefa de reconhecimento de objetos. Os animais expostos ao EA despendem menos tempo explorando os objetos, tanto familiar como não familiar e apresentam igual capacidade de discriminar os objetos. Estes resultados demonstram um comportamento mais propicio a sobrevivência da espécie em animais expostos ao EA, o que inclui uma rápida exploração e um possível aumento na capacidade de aprender sobre o ambiente. / Environmental enrichment (EE) induces plastic changes in the brain, including morphological changes in hippocampal neurons, with increases in synaptic and spine densities. In recent years, the evidence for a role of astrocytes in regulating synaptic transmission and plasticity has increased, and it is likely that morphological and functional changes in astrocytes play an important role in brain plasticity. In others hand EE is used to investigate behavioral modifications associated with gene-environmental interaction. Our study was designed to evaluate changes in astrocytes induced by EE in the hippocampus, focusing on astrocytic density and on morphological changes in astrocytic processes and the performance in object recognition task (ORT) for evaluate animals ability to learn about their environment. After 8 weeks of EE starting at weaning, CF-1 mice presented no significant changes in astrocyte number or in the density of glial fibrillary acidic protein immunoreactivity (GFAP-ir) in the Stratum Radiatum. However, in the same region occur significant increase in the ramification of astrocytic processes, as well as by an increase in the number and length of primary processes extending in a parallel orientation to CA1 nerve fibers. This led astrocytes to acquire a more stellate morphology, a fact which could be related to the increase in hippocampal synaptic density observed in previous studies. These findings corroborate the idea that structural changes in astrocytic networks are an integral part of plasticity processes occurring in the brain. In other hand, our results indicate that EE decreased the time the animals spent exploring familiar and unfamiliar objects and total time spent exploring both objects, without affecting the capacity of discrimination of objects. These findings indicate a more propitious behavior for species survival in animals subjected to EE, including rapid exploration and learning about the environment.

O termo neuroplasticidade se refere às mudanças funcionais ou anatômicas que ocorrem no sistema nervoso decorrentes de experiências. O enriquecimento ambiental (EA) é um dos modelos experimentais utilizados para estudar eventos relacionados à neuroplasticidade, pois aumenta a neurogênese, os níveis de neurotrofinas, a sobrevivência neuronal, a sinaptogênese, além de induzir cascatas de sinalização e uma mudança na arborização dendritica dos neurônios de diversas regiões encefálicas. Nos últimos anos a importância dos astrócitos tem ganho destaque, principalmente no que tange a sua capacidade de modular sinapses e na participação ativa em eventos plásticos no encéfalo, essas mudanças estão relacionadas a alterações funcionais e morfológicas. O EA é um modelo interessante para avaliar performances comportamentais pois é um modelo que ocasiona mudanças na capacidade de armazenar e acessar novas informações. Sendo assim, acreditamos que o EA é capaz de gerar efeitos plásticos nos astrócitos do Stratum Radiatum da região CA1 e alterar as respostas comportamentais a tarefa de reconhecimento de objetos. Após oito semanas de EA iniciado logo após o desmame, camundongos CF-1 albinos não apresentam diferenças significativas no número de astrócitos GFAP-ir e na densidade óptica para GFAP no Stratum Radiatum. Entretanto ocorreu um aumento no número de processos originários do soma, aumento no tamanho destes processos no eixo lateral, paralelo as colaterais de Schaffer e no número de intersecções aos círculos concêntricos de Scholl na mesma região. Os astrócitos adquirem um formato estrelado, este achado pode estar relacionado ao aumento da densidade sináptica nesta região e corroboram com a idéia de que modificações astrocitárias são parte ativa dos processos plásticos que ocorrem no encéfalo. Nossos resultados mostraram também uma mudança comportamental na resposta a tarefa de reconhecimento de objetos. Os animais expostos ao EA despendem menos tempo explorando os objetos, tanto familiar como não familiar e apresentam igual capacidade de discriminar os objetos. Estes resultados demonstram um comportamento mais propicio a sobrevivência da espécie em animais expostos ao EA, o que inclui uma rápida exploração e um possível aumento na capacidade de aprender sobre o ambiente. / Environmental enrichment (EE) induces plastic changes in the brain, including morphological changes in hippocampal neurons, with increases in synaptic and spine densities. In recent years, the evidence for a role of astrocytes in regulating synaptic transmission and plasticity has increased, and it is likely that morphological and functional changes in astrocytes play an important role in brain plasticity. In others hand EE is used to investigate behavioral modifications associated with gene-environmental interaction. Our study was designed to evaluate changes in astrocytes induced by EE in the hippocampus, focusing on astrocytic density and on morphological changes in astrocytic processes and the performance in object recognition task (ORT) for evaluate animals ability to learn about their environment. After 8 weeks of EE starting at weaning, CF-1 mice presented no significant changes in astrocyte number or in the density of glial fibrillary acidic protein immunoreactivity (GFAP-ir) in the Stratum Radiatum. However, in the same region occur significant increase in the ramification of astrocytic processes, as well as by an increase in the number and length of primary processes extending in a parallel orientation to CA1 nerve fibers. This led astrocytes to acquire a more stellate morphology, a fact which could be related to the increase in hippocampal synaptic density observed in previous studies. These findings corroborate the idea that structural changes in astrocytic networks are an integral part of plasticity processes occurring in the brain. In other hand, our results indicate that EE decreased the time the animals spent exploring familiar and unfamiliar objects and total time spent exploring both objects, without affecting the capacity of discrimination of objects. These findings indicate a more propitious behavior for species survival in animals subjected to EE, including rapid exploration and learning about the environment.

Apresentamos neste trabalho o desenvolvimento de um método para o reconhecimento do Formato de Objetos Tri-dimensionais. Os sistemas tradicionais de Visão Computacional empregam imagens bi-dimensionais obtidos através de câmeras de TV, ricas em detalhes necessários a visão humana. Estes detalhes em grande parte das aplicações industriais de Robôs são supérfluos. Os algoritmos tradicionais de classificação consomem portanto muito tempo no processamento deste excesso de informação. Para este trabalho, desenvolvemos um sistema dedicado para reconhecimento que utiliza um feixe de Laser defletido sobre um objeto e a digitalização da Luminância em cada ponto de sua superfície. A intensidade luminosa refletida e proporcional a distância do ponto ao observador. É, portanto, possível determinar parâmetros que classifiquem cada objeto. A inclinação de cada face de um poliedro, o comportamento de suas fronteiras e também a existência de arestas internas, são as características adotadas. Estas características são então rotuladas, permitindo que o programa de classificação busque em um \"banco de conhecimento\" previamente estabelecido, a descrição dos objetos. Uma mesa giratória permite a rotação do modele fornecendo novas vistas ao observador, determinando sua classificação. Todo o sistema é controlado por um microcomputador cujo programa reconhece em tempo real o objeto em observação. Para o protótipo construído, utilizamos um Laser de HeNe sendo a recepção do raio refletido realizada por um fototransistor. Os objetos reconhecíveis pelo programa são poliedros regulares simples, compondo o seguinte conjunto: 1 prisma de base triangular, 1 cubo, 1 pirâmide de base triangular, 1 pirâmide de base retangular. O tratamento matemático empregado visa a comprovação da tecnologia proposta, podendo, na continuação de trabalhos futuros, ser efetivamente estendido a diversos outros objetos como, por exemplo, os de superfícies curvas. / We present in this work a new method for three dimensional Shape Recognition. Traditional Computer Vision systems use bi-dimensional TV camera images. In most of the industrial Robotic applications, the excess of detail obtained by the TV camera is needless. Traditional classification algorithms spend a lot of time to process the excess of information. For the present work we developed a dedicated recognition system, which deflects a Laser beam over an object and digitizes the Reflected beam point by point over the surface. The intensity of the reflected beam is proportional to the observer distance. Using this technique it was possible to establish features to classify various objects. These features are the slope of the polyhedral surfaces, the boundary type and the inner edges. For each object the features are labeled and the classification algorithm searches in a \"knowledge data base\" for the object description. The recognition system used a He-Ne Laser and the reflected signal was captured by a photo-transistor. The object to be recognized is placed over a rotating table which can be rotated, supplying a new view for the classification. A microcomputer controls the system operation and the object is recognized in real time. The recognized objects were simple regular polyhedral, just as: 1 triangular base prism, 1 cube, 1 triangular base pyramid, 1 rectangular base pyramid. To check that the proposed technology was correct, we used a dedicated mathematical approach, which can be extended to other surfaces, such as curves, in future works.

Shape-based matching (SBM) is a method for matching objects in greyscale images. It extracts edges from search images and matches them to a model using a similarity measure. In this thesis we extend SBM to find the tilt and height position of the object in addition to the z-plane rotation and x-y-position. The search is conducted using a scale pyramid to improve the search speed. A 3D matching can be done for small tilt angles by using SBM on height data and extending it with additional steps to calculate the tilt of the object. The full pose is useful for picking objects with an industrial robot. The tilt of the object is calculated using a RANSAC plane estimator. After the 2D search the differences in height between all corresponding points of the model and the live image are calculated. By estimating a plane to this difference the tilt of the object can be calculated. Using the tilt the model edges are tilted in order to improve the matching at the next scale level. The problems that arise with occlusion and missing data have been studied. Missing data and erroneous data have been thresholded manually after conducting tests where automatic filling of missing data did not noticeably improve the matching. The automatic filling could introduce new false edges and remove true ones, thus lowering the score. Experiments have been conducted where objects have been placed at increasing tilt angles. The results show that the matching algorithm is object dependent and correct matches are almost always found for tilt angles less than 10 degrees. This is very similar to the original 2D SBM because the model edges does not change much for such small angels. For tilt angles up to about 25 degrees most objects can be matched and for nice objects correct matches can be done at large tilt angles of up to 40 degrees.

O termo neuroplasticidade se refere às mudanças funcionais ou anatômicas que ocorrem no sistema nervoso decorrentes de experiências. O enriquecimento ambiental (EA) é um dos modelos experimentais utilizados para estudar eventos relacionados à neuroplasticidade, pois aumenta a neurogênese, os níveis de neurotrofinas, a sobrevivência neuronal, a sinaptogênese, além de induzir cascatas de sinalização e uma mudança na arborização dendritica dos neurônios de diversas regiões encefálicas. Nos últimos anos a importância dos astrócitos tem ganho destaque, principalmente no que tange a sua capacidade de modular sinapses e na participação ativa em eventos plásticos no encéfalo, essas mudanças estão relacionadas a alterações funcionais e morfológicas. O EA é um modelo interessante para avaliar performances comportamentais pois é um modelo que ocasiona mudanças na capacidade de armazenar e acessar novas informações. Sendo assim, acreditamos que o EA é capaz de gerar efeitos plásticos nos astrócitos do Stratum Radiatum da região CA1 e alterar as respostas comportamentais a tarefa de reconhecimento de objetos. Após oito semanas de EA iniciado logo após o desmame, camundongos CF-1 albinos não apresentam diferenças significativas no número de astrócitos GFAP-ir e na densidade óptica para GFAP no Stratum Radiatum. Entretanto ocorreu um aumento no número de processos originários do soma, aumento no tamanho destes processos no eixo lateral, paralelo as colaterais de Schaffer e no número de intersecções aos círculos concêntricos de Scholl na mesma região. Os astrócitos adquirem um formato estrelado, este achado pode estar relacionado ao aumento da densidade sináptica nesta região e corroboram com a idéia de que modificações astrocitárias são parte ativa dos processos plásticos que ocorrem no encéfalo. Nossos resultados mostraram também uma mudança comportamental na resposta a tarefa de reconhecimento de objetos. Os animais expostos ao EA despendem menos tempo explorando os objetos, tanto familiar como não familiar e apresentam igual capacidade de discriminar os objetos. Estes resultados demonstram um comportamento mais propicio a sobrevivência da espécie em animais expostos ao EA, o que inclui uma rápida exploração e um possível aumento na capacidade de aprender sobre o ambiente. / Environmental enrichment (EE) induces plastic changes in the brain, including morphological changes in hippocampal neurons, with increases in synaptic and spine densities. In recent years, the evidence for a role of astrocytes in regulating synaptic transmission and plasticity has increased, and it is likely that morphological and functional changes in astrocytes play an important role in brain plasticity. In others hand EE is used to investigate behavioral modifications associated with gene-environmental interaction. Our study was designed to evaluate changes in astrocytes induced by EE in the hippocampus, focusing on astrocytic density and on morphological changes in astrocytic processes and the performance in object recognition task (ORT) for evaluate animals ability to learn about their environment. After 8 weeks of EE starting at weaning, CF-1 mice presented no significant changes in astrocyte number or in the density of glial fibrillary acidic protein immunoreactivity (GFAP-ir) in the Stratum Radiatum. However, in the same region occur significant increase in the ramification of astrocytic processes, as well as by an increase in the number and length of primary processes extending in a parallel orientation to CA1 nerve fibers. This led astrocytes to acquire a more stellate morphology, a fact which could be related to the increase in hippocampal synaptic density observed in previous studies. These findings corroborate the idea that structural changes in astrocytic networks are an integral part of plasticity processes occurring in the brain. In other hand, our results indicate that EE decreased the time the animals spent exploring familiar and unfamiliar objects and total time spent exploring both objects, without affecting the capacity of discrimination of objects. These findings indicate a more propitious behavior for species survival in animals subjected to EE, including rapid exploration and learning about the environment.

Cette thèse a pour objectif de proposer un système de reconnaissance d’images utilisant des informations attentionnelles. Nous nous intéressons à la capacité d’une telle approche à améliorer la complexité en temps de calcul et en utilisation mémoire pour la reconnaissance d’objets. Dans un premier temps, nous avons proposé d’utiliser un système d’attention visuelle comme filtre pour réduire le nombre de points d’intérêt générés par les détecteurs traditionnels [Awad 12]. En utilisant l’architecture attentionnelle proposée par Perreira da Silva comme filtre [Awad 12] sur la base d’images de VOC 2005, nous avons montré qu’un filtrage de 60% des points d’intérêt (extraits par Harris-Laplace et Laplacien) ne fait diminuer que légèrement la performance d’un système de reconnaissance d’objets (différence moyenne de AUC ~ 1%) alors que le gain en complexité est important (40% de gain en vitesse de calcul et 60% en complexité). Par la suite, nous avons proposé un descripteur hybride perceptuel-texture [Awad 14] qui caractérise les informations fréquentielles de certaines caractéristiques considérées comme perceptuellement intéressantes dans le domaine de l’attention visuelle, comme la couleur, le contraste ou l’orientation. Notre descripteur a l’avantage de fournir des vecteurs de caractéristiques ayant une dimension deux fois moindre que celle des descripteurs proposés dans l’état de l’art. L’expérimentation de ce descripteur sur un système de reconnaissance d’objets (le détecteur restant SIFT), sur la base d’images de VOC 2007, a montré une légère baisse de performance (différence moyenne de précision ~5%) par rapport à l’algorithme original, basé sur SIFT mais gain de 50% en complexité. Pour aller encore plus loin, nous avons proposé une autre expérimentation permettant de tester l’efficacité globale de notre descripteur en utilisant cette fois le système d’attention visuelle comme détecteur des points d’intérêt sur la base d’images de VOC 2005. Là encore, le système n’a montré qu’une légère baisse de performance (différence moyenne de précision ~3%) alors que la complexité est réduite de manière drastique (environ 50% de gain en temps de calcul et 70% en complexité). / The main objective of this thesis is to propose a pipeline for an object recognition algorithm, near to human perception, and at the same time, address the problems of Content Based image retrieval (CBIR) algorithm complexity : query run time and memory allocation. In this context, we propose a filter based on visual attention system to select salient points according to human interests from the interest points extracted by a traditionnal interest points detectors. The test of our approach, using Perreira Da Silva’s system as filter, on VOC 2005 databases, demonstrated that we can maintain approximately the same performance of a object recognition system by selecting only 40% of interest points (extracted by Harris-Laplace and Laplacian), while having an important gain in complexity (40% gain in query-run time and 60% in complexity). Furthermore, we address the problem of high dimensionality of descriptor in object recognition system. We proposed a new hybrid texture descriptor, representing the spatial frequency of some perceptual features extracted by a visual attention system. This descriptor has the advantage of being lower dimension vs. traditional descriptors. Evaluating our descriptor with an object recognition system (interest points detectors are Harris-Laplace & Laplacian) on VOC 2007 databases showed a slightly decrease in the performance (with 5% loss in Average Precision) compared to the original system, based on SIFT descriptor (with 50% complexity gain). In addition, we evaluated our descriptor using a visual attention system as interest point detector, on VOC 2005 databases. The experiment showed a slightly decrease in performance (with 3% loss in performance), meanwhile we reduced drastically the complexity of the system (with 50% gain in run-query time and 70% in complexity).

Abstract Pattern recognition and registration are integral elements of computer vision, which considers image patterns. This thesis presents novel blur, and combined blur and geometric invariant features for pattern recognition and registration related to images. These global or local features are based on the Fourier transform phase, and are invariant or insensitive to image blurring with a centrally symmetric point spread function which can result, for example, from linear motion or out of focus. The global features are based on the even powers of the phase-only discrete Fourier spectrum or bispectrum of an image and are invariant to centrally symmetric blur. These global features are used for object recognition and image registration. The features are extended for geometrical invariances up to similarity transformation: shift invariance is obtained using bispectrum, and rotation-scale invariance using log-polar mapping of bispectrum slices. Affine invariance can be achieved as well using rotated sets of the log-log mapped bispectrum slices. The novel invariants are shown to be more robust to additive noise than the earlier blur, and combined blur and geometric invariants based on image moments. The local features are computed using the short term Fourier transform in local windows around the points of interest. Only the lowest horizontal, vertical, and diagonal frequency coefficients are used, the phase of which is insensitive to centrally symmetric blur. The phases of these four frequency coefficients are quantized and used to form a descriptor code for the local region. When these local descriptors are used for texture classification, they are computed for every pixel, and added up to a histogram which describes the local pattern. There are no earlier textures features which have been claimed to be invariant to blur. The proposed descriptors were superior in the classification of blurred textures compared to a few non-blur invariant state of the art texture classification methods.

The human vision system is generally recognized as being superior to all known artificial vision systems. Visual attention, among many processes that are related to human vision, is responsible for identifying relevant regions in a scene for further processing. In most cases, analyzing an entire scene is unnecessary and inevitably time consuming. Hence considering visual attention might be advantageous. A subfield of computer vision where this particular functionality is computationally emulated has been shown to retain high potential in solving real world vision problems effectively. In this monograph, elements of visual attention are explored and algorithms are proposed that exploit such elements in order to enhance image understanding capabilities. Satellite images are given special attention due to their practical relevance, inherent complexity in terms of image contents, and their resolution. Processing such large-size images using visual attention can be very helpful since one can first identify relevant regions and deploy further detailed analysis in those regions only. Bottom-up features, which are directly derived from the scene contents, are at the core of visual attention and help identify salient image regions. In the literature, the use of intensity, orientation and color as dominant features to compute bottom-up attention is ubiquitous. The effects of incorporating an entropy feature on top of the above mentioned ones are also studied. This investigation demonstrates that such integration makes visual attention more sensitive to fine details and hence retains the potential to be exploited in a suitable context. One interesting application of bottom-up attention, which is also examined in this work, is that of image segmentation. Since low salient regions generally correspond to homogenously textured regions in the input image; a model can therefore be learned from a homogenous region and used to group similar textures existing in other image regions. Experimentation demonstrates that the proposed method produces realistic segmentation on satellite images. Top-down attention, on the other hand, is influenced by the observer’s current states such as knowledge, goal, and expectation. It can be exploited to locate target objects depending on various features, and increases search or recognition efficiency by concentrating on the relevant image regions only. This technique is very helpful in processing large images such as satellite images. A novel algorithm for computing top-down attention is proposed which is able to learn and quantify important bottom-up features from a set of training images and enhances such features in a test image in order to localize objects having similar features. An object recognition technique is then deployed that extracts potential target objects from the computed top-down attention map and attempts to recognize them. An object descriptor is formed based on physical appearance and uses both texture and shape information. This combination is shown to be especially useful in the object recognition phase. The proposed texture descriptor is based on Legendre moments computed on local binary patterns, while shape is described using Hu moment invariants. Several tools and techniques such as different types of moments of functions, and combinations of different measures have been applied for the purpose of experimentations. The developed algorithms are generalized, efficient and effective, and have the potential to be deployed for real world problems. A dedicated software testing platform has been designed to facilitate the manipulation of satellite images and support a modular and flexible implementation of computational methods, including various components of visual attention models.

Le travail de recherche de cette thèse de doctorat s'inscrit dans le cadre du suivi médical des patients atteints de démences liées à l'âge à l'aide des caméras videos portées par les patients. L'idée est de fournir aux médecins un nouvel outil pour le diagnostic précoce de démences liées à l'âge telles que la maladie d'Alzheimer. Plus précisément, les Activités Instrumentales du Quotidien (IADL: Instrumental Activities of Daily Living en anglais) doivent être indexées automatiquement dans les vidéos enregistrées par un dispositif d'enregistrement portable.Ces vidéos présentent des caractéristiques spécifiques comme de forts mouvements ou de forts changements de luminosité. De plus, la tâche de reconnaissance visée est d'un très haut niveau sémantique. Dans ce contexte difficile, la première étape d'analyse est la définition d'un équivalent à la notion de « plan » dans les contenus vidéos édités. Nous avons ainsi développé une méthode pour le partitionnement d'une vidéo tournée en continu en termes de « points de vue » à partir du mouvement apparent.Pour la reconnaissance des IADL, nous avons développé une solution selon le formalisme des Modèles de Markov Cachés (MMC). Un MMC hiérarchique à deux niveaux a été introduit, modélisant les activités sémantiques ou des états intermédiaires. Un ensemble complexe de descripteurs (dynamiques, statiques, de bas niveau et de niveau intermédiaire) a été exploité et les espaces de description joints optimaux ont été identifiés expérimentalement.Dans le cadre de descripteurs de niveau intermédiaire pour la reconnaissance d'activités nous nous sommes particulièrement intéressés aux objets sémantiques que la personne manipule dans le champ de la caméra. Nous avons proposé un nouveau concept pour la description d'objets ou d'images faisant usage des descripteurs locaux (SURF) et de la structure topologique sous-jacente de graphes locaux. Une approche imbriquée pour la construction des graphes où la même scène peut être décrite par plusieurs niveaux de graphes avec un nombre de nœuds croissant a été introduite. Nous construisons ces graphes par une triangulation de Delaunay sur des points SURF, préservant ainsi les bonnes propriétés des descripteurs locaux c'est-à-dire leur invariance vis-à-vis de transformations affines dans le plan image telles qu'une rotation, une translation ou un changement d'échelle.Nous utilisons ces graphes descripteurs dans le cadre de l'approche Sacs-de-Mots-Visuels. Le problème de définition d'une distance, ou dissimilarité, entre les graphes pour la classification non supervisée et la reconnaissance est nécessairement soulevé. Nous proposons une mesure de dissimilarité par le Noyau Dépendant du Contexte (Context-Dependent Kernel: CDK) proposé par H. Sahbi et montrons sa relation avec la norme classique L2 lors de la comparaison de graphes triviaux (les points SURF).Pour la reconnaissance d'activités par MMC, les expériences sont conduites sur le premier corpus au monde de vidéos avec caméra portée destiné à l'observation des d'IADL et sur des bases de données publiques comme SIVAL et Caltech-101 pour la reconnaissance d'objets. / The research of this PhD thesis is fulfilled in the context of wearable video monitoring of patients with aged dementia. The idea is to provide a new tool to medical practitioners for the early diagnosis of elderly dementia such as the Alzheimer disease. More precisely, Instrumental Activities of Daily Living (IADL) have to be indexed in videos recorded with a wearable recording device.Such videos present specific characteristics i.e. strong motion or strong lighting changes. Furthermore, the tackled recognition task is of a very strong semantics. In this difficult context, the first step of analysis is to define an equivalent to the notion of “shots” in edited videos. We therefore developed a method for partitioning continuous video streams into viewpoints according to the observed motion in the image plane.For the recognition of IADLs we developed a solution based on the formalism of Hidden Markov Models (HMM). A hierarchical HMM with two levels modeling semantic activities or intermediate states has been introduced. A complex set of features (dynamic, static, low-level, mid-level) was proposed and the most effective description spaces were identified experimentally.In the mid-level features for activities recognition we focused on the semantic objects the person manipulates in the camera view. We proposed a new concept for object/image description using local features (SURF) and the underlying semi-local connected graphs. We introduced a nested approach for graphs construction when the same scene can be described by levels of graphs with increasing number of nodes. We build these graphs with Delaunay triangulation on SURF points thus preserving good properties of local features i.e. the invariance with regard to affine transformation of image plane: rotation, translation and zoom.We use the graph features in the Bag-of-Visual-Words framework. The problem of distance or dissimilarity definition between graphs for clustering or recognition is obviously arisen. We propose a dissimilarity measure based on the Context Dependent Kernel of H. Sahbi and show its relation with the classical entry-wise norm when comparing trivial graphs (SURF points).The experiments are conducted on the first corpus in the world of wearable videos of IADL for HMM based activities recognition, and on publicly available academic datasets such as SIVAL and Caltech-101 for object recognition.