Dans cette thèse, nous explorons premièrement la notion de directionnalité lors de la conception de repères d'ondelettes du plan. Cette propriété, qui semble essentielle pour la vision biologique, donne lieu à une meilleure représentation des contours d'ob jets dans les décompositions d'images utilisant ces repères. Elle génère en outre une redondance supplémentaire qui, exploitée à bon escient, permet par exemple de réduire les effets d'un bruit additif (gaussien). Nous montrons également comment cette directionnalité, généralement perçue comme un paramètre figé, peut être adaptée localement aux éléments d'une image. Nous définissons ainsi le concept d'analyse d'images multisélective. Dans ce cadre, des règles de récurrence héritées d'une analyse multirésolution circulaire associent des ondelettes d'une certaine sélectivité angulaire pour générer des ondelettes de plus faible directionnalité jusqu'à l'obtention d'une ondelette totalement isotrope. Dans le cas d'un repère d'ondelettes linéaire, ces différents niveaux de ...

L¢objectif de cette these consiste en l¢etude et la construction de nouvelles transformees scalables mises en jeu dans le schema de codage video t+2D, afin d¢ameliorer le gain en compression. L¢utilisation du formalisme lifting lors de la construction de ces transformees spatio-temporelles permet l¢introduction d¢operateurs non-lineaires, particulierement utiles pour representer efficacement les singularites et discontinuites presentes dans une sequence video. Nous nous interessons dans un premier temps a l¢optimisation et a la construction de nouvelles transformees temporelles compensees en mouvement, afin d¢augmenter l¢efficacite de codage objective et subjective. Nous envisageons ensuite l¢elaboration et la mise en place de bancs de filtres M-bandes pour decomposer spatialement les sous-bande temporelles. Nous traitons alors de l¢extension des proprietes de scalabilite du banc de synthese M-bandes a des facteurs rationnels quelconques. Enfin, nous decrivons la construction de decompositions spatiales en ondelettes adaptatives, non-lineaires et ...

In French. See thesis pdf.

Matrices, as natural representation of linear mappings in finite dimension, play a crucial role in signal processing and machine learning. Multiplying a vector by a full rank matrix a priori costs of the order of the number of non-zero entries in the matrix, in terms of arithmetic operations. However, matrices exist that can be applied much faster, this property being crucial to the success of certain linear transformations, such as the Fourier transform or the wavelet transform. What is the property that allows these matrices to be applied rapidly ? Is it easy to verify ? Can weapproximate matrices with ones having this property ? Can we estimate matrices having this property ? This thesis investigates these questions, exploring applications such as learning dictionaries with efficient implementations, accelerating the resolution of inverse problems or Fast Fourier Transform on graphs.

An intelligent antenna is actually composed of a series of elementary antennas (linear, circular, etc.) who’s received signals are balanced and combined by using a technique of adaptation in order to control and improve the reception or the transmission. The objective of our study is to look further into the knowledge of the methods of formation of the beams and to elaborate a digital technique of synthesis for the formation of ways and the cancellation of interfering which answers the specifications imposed by the system adaptive by using the networks of neurons. The work of thesis consisted of a contribution to the optimization of the lobe of radiation for an intelligent antenna. In a first stage, two complementary approaches were developed to implement the technique of forming of the lobe, one based on an algorithm of optimization which calculates the ...

The goal of this work is to propose a general approach for surveillance video indexing and retrieval. Based on the hypothesis that videos are preprocessed by an external video analysis module, this approach is composed of two phases : indexing phase and retrieval phase. In order to profit from the output of various video analysis modules, a general data model consisting of two main concepts, objects and events, is proposed. The indexing phase that aims at preparing data defined in the data model performs three tasks. Firstly, two new key blob detection methods in the object representation task choose for each detected object a set of key blobs associated with a weight. Secondly, the feature extraction task analyzes a number of visual and temporal features on detected objects. Finally, the indexing task computes attributes of the two concepts and stores ...

The amount of digital music available both on the Internet and by each listener has considerably raised for about ten years. The organization and the accessibillity of this amount of data demand that additional informations are available, such as artist, album and song names, musical genre, tempo, mood or other symbolic or semantic attributes. Automatic music indexing has thus become a challenging research area. If some tasks are now correctly handled for certain types of music, such as automatic genre classification for stereotypical music, music instrument recoginition on solo performance and tempo extraction, others are more difficult to perform. For example, automatic transcription of polyphonic signals and instrument ensemble recognition are still limited to some particular cases. The goal of our study is not to obain a perfect transcription of the signals and an exact classification of all the instruments ...

This thesis interests in different methods of image compression combining both Bayesian aspects and ``sparse decomposition'' aspects. Two compression methods are in particular investigated. Transform coding, first, is addressed from a transform optimization point of view. The optimization is considered at two levels: in the spatial domain by adapting the support of the transform, and in the transform domain by selecting local bases among finite sets. The study of bases learned with an algorithm from the literature constitutes an introduction to a novel learning algorithm, which encourages the sparsity of the decompositions. Predictive coding is then addressed. Motivated by recent contributions based on sparse decompositions, we propose a novel Bayesian prediction algorithm based on mixtures of sparse decompositions. Finally, these works allowed to underline the interest of structuring the sparsity of the decompositions. For example, a weighting of the decomposition ...

This work deals with the use of an antenna array at the base station of a mobile communication system for transmission. In reception, solutions that exploit the antenna array are now well established. In transmission, however, the problem remains open. Two approaches are possible : exploit the array by using beamforming techniques or by using diversity techniques. These two approaches are based on opposite assumptions about the channels correlation, which implies a greater or smaller distance between antennas, depending on the environment. In practice, these assumptions are not verified. Here, we aim to deal with the problem as a whole for better exploiting the antenna array. This work treats the mono-user case, as well as the multi-user scenario. In the mono-user case, we propose a transmission scheme composed of a classical transmit diversity technique applied to virtual antennas, which are ...

Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image Retrieval (CBIR). In contrast with most purely technological approaches, the thesis Human-Centered Content-Based Image Retrieval approaches the problem from a human/user centered perspective. Psychophysical experiments were conducted in which people were asked to categorize colors. The data gathered from these experiments was fed to a Fast Exact Euclidean Distance (FEED) transform (Schouten & Van den Broek, 2004), which enabled the segmentation of color space based on human perception (Van den Broek et al., 2008). This unique color space segementation was exploited for texture analysis and image segmentation, and subsequently for full-featured CBIR. In addition, a unique CBIR-benchmark was developed (Van ...

Alzheimer’s disease (AD) is a neurodegenerative terminal disorder that accounts for nearly 70% of dementia cases worldwide. Global dementia incidence is projected to 75 million cases by 2030, with the majority of the affected individuals coming from low- and medium- income countries. Although there is no cure for AD, early diagnosis can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using mental status examinations, expensive neuroimaging scans, and invasive laboratory tests, all of which render the diagnosis time-consuming and costly. Notwithstanding, over the last decade electroencephalography (EEG), specifically resting-state EEG (rsEEG), has emerged as an alternative technique for AD diagnosis with accuracies inline with those obtained with more expensive neuroimaging tools, such as magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET). However the use of rsEEG for ...

The present doctoral thesis aims towards the development of new long-term, multi-channel, audio-visual processing techniques for the analysis of bioacoustics phenomena. The effort is focused on the study of the physiology of the gastrointestinal system, aiming at the support of medical research for the discovery of gastrointestinal motility patterns and the diagnosis of functional disorders. The term "processing" in this case is quite broad, incorporating the procedures of signal processing, content description, manipulation and analysis, that are applied to all the recorded bioacoustics signals, the auxiliary audio-visual surveillance information (for the monitoring of experiments and the subjects' status), and the extracted audio-video sequences describing the abdominal sound-field alterations. The thesis outline is as follows. The main objective of the thesis, which is the technological support of medical research, is presented in the first chapter. A quick problem definition is initially ...

In this doctoral thesis several scale-free texture segmentation procedures based on two fractal attributes, the Hölder exponent, measuring the local regularity of a texture, and local variance, are proposed.A piecewise homogeneous fractal texture model is built, along with a synthesis procedure, providing images composed of the aggregation of fractal texture patches with known attributes and segmentation. This synthesis procedure is used to evaluate the proposed methods performance.A first method, based on the Total Variation regularization of a noisy estimate of local regularity, is illustrated and refined thanks to a post-processing step consisting in an iterative thresholding and resulting in a segmentation.After evidencing the limitations of this first approach, deux segmentation methods, with either "free" or "co-located" contours, are built, taking in account jointly the local regularity and the local variance.These two procedures are formulated as convex nonsmooth functional minimization problems.We ...

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily ...

Visual information, in the form of digital images and videos, has become so omnipresent in computer databases and repositories, that it can no longer be considered a “second class citizen”, eclipsed by textual information. In that scenario, image classification has become a critical task. In particular, the pursuit of automatic identification of complex semantical concepts represented in images, such as scenes or objects, has motivated researchers in areas as diverse as Information Retrieval, Computer Vision, Image Processing and Artificial Intelligence. Nevertheless, in contrast to text documents, whose words carry semantic, images consist of pixels that have no semanticinformation by themselves, making the task very challenging. In this dissertation, we have addressed the problem of representing images based on their visual information. Our aim is content-based concept detection in images and videos, with a novel representation that enriches the Bag-of-Words model. ...