Vision par ordinateur | EPFL Graph Search

La vision par ordinateur est un domaine scientifique et une branche de l’intelligence artificielle qui traite de la façon dont les ordinateurs peuvent acquérir une compréhension de haut niveau à partir d's ou de vidéos numériques. Du point de vue de l'ingénierie, il cherche à comprendre et à automatiser les tâches que le système visuel humain peut effectuer. Les tâches de vision par ordinateur comprennent des procédés pour acquérir, traiter, et « comprendre » des images numériques, et extraire des données afin de produire des informations numériques ou symboliques, par ex. sous forme de décisions. Dans ce contexte, la compréhension signifie la transformation d'images visuelles (l'entrée de la rétine) en descriptions du monde qui ont un sens pour les processus de pensée et peuvent susciter une action appropriée. Cette compréhension de l'image peut être vue comme l’acquisition d'informations symboliques à partir de données d'image, par l'emploi de modèles s'apuyant sur la géométri

Robust visual tracking using feature selection

Fabien Willemin

Visual tracking has become a very important component in computer vision, but achieving a robust, reliable and real time tracking remains a real challenge.In order to improve the actual state-of-the-art, we choose to study and improve one of the most performing adaptive tracker by detection. We selected Struck [27] for this quality performance and his low computational cost that makes it real time. Inspired by the great successes of binary keypoint descriptors, we choose to apply binary description to a patch. We propose to use Multi-Block Local Binary Pattern (MB-LBP), based on its great success in face detection and description. In this work we present a technique for selecting the best features for tracking. In combination with the feature selection we propose a technique to take into account contextual information in order to increase the robustness of the tracker. We propose a solution to add scale adaptation to the algorithm, and suggest to transpose this technique to add rotation adaptation. Experimentally we validate these techniques showing that we outperform the state-of-art racking algorithms. To do that we use a benchmarking tool using 51 videos and compare our algorithm to 29 algorithms.

2014

Unsupervised multiscale image segmentation

Ana Dimitrijevic

In the early stages of visual information processing one of the most fundamental and complex task is segmentation. This process is used to divide images or image sequences into meaningful regions or objects. Such a representation is definitely needed to go from the first rough description of the scene (i.e. light intensity) up to the semantic level, where one is able to discriminate shapes and other high-level visual primitives. The biological visual system performs this extremely difficult task with such ease that is still a major challenge for computer vision to reach this level of speed and accuracy. It is by now widely believed that a method inspired by biological vision has the best chances of accomplishing this goal. Therefore, our main goal in this thesis is to step on biologically inspired schemes to propose new and efficient techniques to solve the image segmentation problem. The starting point and key concept in this research, is the multiresolution paradigm. Indeed, the fact that the Human Visual System (HVS) processes the information in a multiresolution fashion is now well established. Several techniques have been introduced to model this behaviour. Scale-space is one of them and we will thus focus on such a formulation of the segmentation problem. Such a representation is composed by a stack of successive versions of the original data set at coarser scales. It is assumed that, the bigger the scale, the less information referred to local characteristics of the input data will appear. We also impose that general information applying to large scales will last through scale. Taking that into account, it is reasonable to think that local and high resolution scale information can be related to general and low resolution information. This will enable us to extract image structures. We thus propose to link the multiresolution approach to a computational model based on the analysis of the entire range of significant information across scales. The inherent multiscale image structure of the non-linear scale-space guides a coarse to fine segmentation using different strategies. The first one might be termed extrinsic because the non-linear scale-space is analyzed by an external process of linking information through scales. This leads to a completely adaptive scheme that can then be coupled to a labeling procedure in order to define a genuine segmentation. The second one could be labeled intrinsic because it adapts to the image through its inner properties and incorporate this in the labeling step of the main algorithm. These two approaches are particular examples of multiscale image segmentation. We also apply the same scale-space concept to define a segmentation variational formulation that leads to a region merging segmentation. In order to illustrate the proposed segmentation schemes, we apply our algorithms to gray-scale, noisy, color and texture images. Finally, it should be mentioned that the main problem addressed in this thesis, general image segmentation, plays an important role in the image understanding process, especially those that involve natural scenes rich in color and texture. The potential applications of the algorithms are thus numerous: medical image analysis, interactive multimedia developments, object based coding and compression, surveillance applications, image content based browsing and retrieval, etc.

EPFL2006

Constraint consistency techniques for continuous domains

Jamila Sam

Constraint Satisfaction Problems (CSPs) are ubiquitous in computer science. Many problems, ranging from resource allocation and scheduling to fault diagnosis and design, involve constraint satisfaction as an essential component. A CSP is given by a set of variables and constraints on small subsets of these variables. It is solved by finding assignments of values to the variables such that all constraints are satisfied. In its most general form, a CSP is combinatorial and complex. In this thesis, we consider constraint satisfaction problems with variables in continuous, numerical domains. Contrary to most existing techniques, which focus on computing a single optimal solution, we address the problem of computing a compact representation of the space of all solutions that satisfy the constraints. This has the advantage that no optimization criterion has to be formulated beforehand, and that the space of possibilities can be explored systematically. In certain applications, such as diagnosis and design, these advantages are crucial. In consistency techniques, the solution space is represented by labels assigned to individual variables or combinations of variables. When the labeling is globally consistent, each label contains only those values or combinations of values which appear in at least one solution. This kind of labeling is a compact, sound and complete representation of the solution space, and can be combined with other reasoning methods. In practice, computing a globally consistent labeling is too complex. This is usually tackled in two ways. One way is to enforce consistencies locally, using propagation algorithms. This prunes the search space and hence reduces the subsequent search effort. The other way is to identify simplifying properties which guarantee that global consistency can be enforced tractably using local propagation algorithms. When constraints are represented by mathematical expressions, implementing local consistency algorithms is difficult because it requires tools for solving arbitrary systems of equations. In this thesis, we propose to approximate feasible solution regions by 2k-trees, thus providing a means of combining constraints logically rather than numerically. This representation, commonly used in computer vision and image processing, avoids using complex mathematical tools. We propose simple and stable algorithms for computing labels of arbitrary degrees of consistency using this representation. For binary constraints, it is known that simplifying convexity properties reduces the complexity of solving a CSP. These properties guarantee that local degrees of consistency are sufficient to ensure global consistency. We show how, in continuous domains, these results can be generalized to ternary and in fact arbitrary n-ary constraints. This leads to polynomial-time algorithms for computing globally consistent labels for a large class of constraint satisfaction problems with continuous variables. We describe and justify our representation of constraints and our consistency algorithms. We also give a complete analysis of the theoretical results we present. Finally, the developed techniques are illustrated using practical examples.

EPFL1995