Segment-based stereo matching algorithm with rectification for single-lens bi-prism stereovision system

Ph.DDOCTOR OF PHILOSOPH

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Contributions à la fusion de segmentations et à l’interprétation sémantique d’images

Cette thèse est consacrée à l’étude de deux problèmes complémentaires, soit la fusion de segmentation d’images et l’interprétation sémantique d’images. En effet, dans un premier temps, nous proposons un ensemble d’outils algorithmiques permettant d’améliorer le résultat final de l’opération de la fusion. La segmentation d’images est une étape de prétraitement fréquente visant à simplifier la représentation d’une image par un ensemble de régions significatives et spatialement cohérentes (également connu sous le nom de « segments » ou « superpixels ») possédant des attributs similaires (tels que des parties cohérentes des objets ou de l’arrière-plan). À cette fin, nous proposons une nouvelle méthode de fusion de segmentation au sens du critère de l’Erreur de la Cohérence Globale (GCE), une métrique de perception intéressante qui considère la nature multi-échelle de toute segmentation de l’image en évaluant dans quelle mesure une carte de segmentation peut constituer un raffinement d’une autre segmentation. Dans un deuxième temps, nous présentons deux nouvelles approches pour la fusion des segmentations au sens de plusieurs critères en nous basant sur un concept très important de l’optimisation combinatoire, soit l’optimisation multi-objectif. En effet, cette méthode de résolution qui cherche à optimiser plusieurs objectifs concurremment a rencontré un vif succès dans divers domaines. Dans un troisième temps, afin de mieux comprendre automatiquement les différentes classes d’une image segmentée, nous proposons une approche nouvelle et robuste basée sur un modèle à base d’énergie qui permet d’inférer les classes les plus probables en utilisant un ensemble de segmentations proches (au sens d’un certain critère) issues d’une base d’apprentissage (avec des classes pré-interprétées) et une série de termes (d’énergie) de vraisemblance sémantique.This thesis is dedicated to study two complementary problems, namely the fusion of image segmentation and the semantic interpretation of images. Indeed, at first we propose a set of algorithmic tools to improve the final result of the operation of the fusion. Image segmentation is a common preprocessing step which aims to simplify the image representation into significant and spatially coherent regions (also known as segments or super-pixels) with similar attributes (such as coherent parts of objects or the background). To this end, we propose a new fusion method of segmentation in the sense of the Global consistency error (GCE) criterion. GCE is an interesting metric of perception that takes into account the multiscale nature of any segmentations of the image while measuring the extent to which one segmentation map can be viewed as a refinement of another segmentation. Secondly, we present two new approaches for merging multiple segmentations within the framework of multiple criteria based on a very important concept of combinatorial optimization ; the multi-objective optimization. Indeed, this method of resolution which aims to optimize several objectives concurrently has met with great success in many other fields. Thirdly, to better and automatically understand the various classes of a segmented image we propose an original and reliable approach based on an energy-based model which allows us to deduce the most likely classes by using a set of identically partitioned segmentations (in the sense of a certain criterion) extracted from a learning database (with pre-interpreted classes) and a set of semantic likelihood (energy) term