ROBUST BACKGROUND SUBTRACTION FOR MOVING CAMERAS AND THEIR APPLICATIONS IN EGO-VISION SYSTEMS

Background subtraction is the algorithmic process that segments out the region of interest often known as foreground from the background. Extensive literature and numerous algorithms exist in this domain, but most research have focused on videos captured by static cameras. The proliferation of portable platforms equipped with cameras has resulted in a large amount of video data being generated from moving cameras. This motivates the need for foundational algorithms for foreground/background segmentation in videos from moving cameras. In this dissertation, I propose three new types of background subtraction algorithms for moving cameras based on appearance, motion, and a combination of them. Comprehensive evaluation of the proposed approaches on publicly available test sequences show superiority of our system over state-of-the-art algorithms. The first method is an appearance-based global modeling of foreground and background. Features are extracted by sliding a fixed size window over the entire image without any spatial constraint to accommodate arbitrary camera movements. Supervised learning method is then used to build foreground and background models. This method is suitable for limited scene scenarios such as Pan-Tilt-Zoom surveillance cameras. The second method relies on motion. It comprises of an innovative background motion approximation mechanism followed by spatial regulation through a Mega-Pixel denoising process. This work does not need to maintain any costly appearance models and is therefore appropriate for resource constraint ego-vision systems. The proposed segmentation combined with skin cues is validated by a novel application on authenticating hand-gestured signature captured by wearable cameras. The third method combines both motion and appearance. Foreground probabilities are jointly estimated by motion and appearance. After the mega-pixel denoising process, the probability estimates and gradient image are combined by Graph-Cut to produce the segmentation mask. This method is universal as it can handle all types of moving cameras

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Tatouage du flux compressé MPEG-4 AVC

La présente thèse aborde le sujet de tatouage du flux MPEG-4 AVC sur ses deux volets théoriques et applicatifs en considérant deux domaines applicatifs à savoir la protection du droit d auteur et la vérification de l'intégrité du contenu. Du point de vue théorique, le principal enjeu est de développer un cadre de tatouage unitaire en mesure de servir les deux applications mentionnées ci-dessus. Du point de vue méthodologique, le défi consiste à instancier ce cadre théorique pour servir les applications visées. La première contribution principale consiste à définir un cadre théorique pour le tatouage multi symboles à base de modulation d index de quantification (m-QIM). La règle d insertion QIM a été généralisée du cas binaire au cas multi-symboles et la règle de détection optimale (minimisant la probabilité d erreur à la détection en condition du bruit blanc, additif et gaussien) a été établie. Il est ainsi démontré que la quantité d information insérée peut être augmentée par un facteur de log2m tout en gardant les mêmes contraintes de robustesse et de transparence. Une quantité d information de 150 bits par minutes, soit environ 20 fois plus grande que la limite imposée par la norme DCI est obtenue. La deuxième contribution consiste à spécifier une opération de prétraitement qui permet d éliminer les impactes du phénomène du drift (propagation de la distorsion) dans le flux compressé MPEG-4 AVC. D abord, le problème a été formalisé algébriquement en se basant sur les expressions analytiques des opérations d encodage. Ensuite, le problème a été résolu sous la contrainte de prévention du drift. Une amélioration de la transparence avec des gains de 2 dB en PSNR est obtenueThe present thesis addresses the MPEG-4 AVC stream watermarking and considers two theoretical and applicative challenges, namely ownership protection and content integrity verification.From the theoretical point of view, the thesis main challenge is to develop a unitary watermarking framework (insertion/detection) able to serve the two above mentioned applications in the compressed domain. From the methodological point of view, the challenge is to instantiate this theoretical framework for serving the targeted applications. The thesis first main contribution consists in building the theoretical framework for the multi symbol watermarking based on quantization index modulation (m-QIM). The insertion rule is analytically designed by extending the binary QIM rule. The detection rule is optimized so as to ensure minimal probability of error under additive white Gaussian noise distributed attacks. It is thus demonstrated that the data payload can be increased by a factor of log2m, for prescribed transparency and additive Gaussian noise power. A data payload of 150 bits per minute, i.e. about 20 times larger than the limit imposed by the DCI standard, is obtained. The thesis second main theoretical contribution consists in specifying a preprocessing MPEG-4 AVC shaping operation which can eliminate the intra-frame drift effect. The drift represents the distortion spread in the compressed stream related to the MPEG encoding paradigm. In this respect, the drift distortion propagation problem in MPEG-4 AVC is algebraically expressed and the corresponding equations system is solved under drift-free constraints. The drift-free shaping results in gain in transparency of 2 dB in PSNREVRY-INT (912282302) / SudocSudocFranceF

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved