Improving the A-Contrario computation of a fundamental matrix in computer vision

Laboratoire MAP5 (Mathématiques appliquées Paris 5), CNRS UMR8145 Université Paris V - Paris DescartesThe fundamental matrix is a two-view tensor playing a central role in Computer Vision geometry. We address its robust estimation given pairs of matched image features, affected by noise and outliers, which searches for a maximal subset of correct matches and the associated fundamental matrix. Overcoming the broadly used parametric RANSAC method, ORSA follows a probabilistic a contrario approach to look for the set of matches being least expected with respect to a uniform random distribution of image points. ORSA lacks performance when this assumption is clearly violated. We will propose an improvement of the ORSA method, based on its same a contrario framework and the use of a non-parametric estimate of the distribution of image features. The role and estimation of the fundamental matrix and the data SIFT matches will be carefully explained with examples. Our proposal performs significantly well for common scenarios of low inlier ratios and local feature concentrations

Image point correspondences and repeated patterns

Matching or tracking interest points between several views is one of the keystones of many computer vision applications. The procedure generally consists in several independent steps, basically interest point extraction, then interest point matching by keeping only the ''best correspondences'' with respect to similarity between some local descriptors, and final correspondence pruning to keep those that are consistent with a realistic camera motion (here, consistent with epipolar constraints or homography transformation.) Each step in itself is a delicate task which may endanger the whole process. In particular, repeated patterns give lots of false correspondences in descriptor-based matching which are hardly, if ever, recovered by the final pruning step. We discuss here the specific difficulties raised by repeated patterns in the point correspondence problem. Then we show to what extent it is possible to address these difficulties. Starting from a statistical model by Moisan and Stival, we propose a one-stage approach for matching interest points based on simultaneous descriptor similarity and geometric constraint. The resulting algorithm has adaptive matching thresholds and is able to pick up point correspondences beyond the nearest neighbour. We also discuss Generalized Ransac and we show how to improve Morel and Yu's Asift, an effective point matching algorithm to make it more robust to the presence of repeated patterns.L'appariement ou le suivi de points d'intérêt entre plusieurs images est la brique de base de nombreuses applications en vision par ordinateur. La procédure consiste généralement en plusieurs étapes indépendantes, à savoir : l'extraction des points d'intérêt, puis l'appariement des points d'intérêt en gardant les "meilleures correspondances" selon la ressemblance de descripteurs locaux, et enfin l'élagage de l'ensemble des correspondances pour garder celles cohérentes avec un mouvement de caméra (ici, cohérentes selon les contraintes épipolaires ou une homographie globale). Chaque étape est une tâche délicate qui peut compromettre le succès du processus entier. En particulier, les motifs répétés génèrent de nombreux faux appariements qui sont difficilement rattrapés par l'élagage final. Dans ce rapport nous discutons les difficultés spécifiques soulevées par les motifs répétés dans l'appariement de points. Ensuite nous montrons dans quelle mesure il est possible de dépasser ces difficultés. En reprenant un modèle statistique proposé par Moisan et Stival, nous proposons une nouvelle approche prenant en compte simultanément la ressemblance des descripteurs et la contrainte géométrique. L'algorithme a des seuils d'appariement adaptatifs et est capable de sélectionner des correspondances au delà du plus proche voisin. Nous discutons aussi Ransac généralisé et nous montrons comment améliorer Asift de Morel et Yu pour le rendre robuste à la présence de motifs répétés

Determining point correspondences between two views under geometric constraint and photometric consistency

Matching or tracking points of interest between several views is one of the keystones of many computer vision applications, especially when considering structure and motion estimation. The procedure generally consists in several independent steps, basically 1) point of interest extraction, 2) point of interest matching by keeping only the ``best correspondences'' with respect to similarity between some local descriptors, 3) correspondence pruning to keep those consistent with an estimated camera motion (here, consistent with epipolar constraints or homography transformation). Each step in itself is a touchy task which may endanger the whole process. In particular, repeated patterns give lots of false matches in step 2) which are hardly, if never, recovered by step 3). Starting from a statistical model by Moisan and Stival, we propose a new one-stage approach to steps 2) and 3), which does not need tricky parameters. The advantage of the proposed method is its robustness to repeated patterns

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

New algorithmic developments in maximum consensus robust fitting

In many computer vision applications, the task of robustly estimating the set of parameters of a geometric model is a fundamental problem. Despite the longstanding research efforts on robust model fitting, there remains significant scope for investigation. For a large number of geometric estimation tasks in computer vision, maximum consensus is the most popular robust fitting criterion. This thesis makes several contributions in the algorithms for consensus maximization. Randomized hypothesize-and-verify algorithms are arguably the most widely used class of techniques for robust estimation thanks to their simplicity. Though efficient, these randomized heuristic methods do not guarantee finding good maximum consensus estimates. To improve the randomize algorithms, guided sampling approaches have been developed. These methods take advantage of additional domain information, such as descriptor matching scores, to guide the sampling process. Subsets of the data that are more likely to result in good estimates are prioritized for consideration. However, these guided sampling approaches are ineffective when good domain information is not available. This thesis tackles this shortcoming by proposing a new guided sampling algorithm, which is based on the class of LP-type problems and Monte Carlo Tree Search (MCTS). The proposed algorithm relies on a fundamental geometric arrangement of the data to guide the sampling process. Specifically, we take advantage of the underlying tree structure of the maximum consensus problem and apply MCTS to efficiently search the tree. Empirical results show that the new guided sampling strategy outperforms traditional randomized methods. Consensus maximization also plays a key role in robust point set registration. A special case is the registration of deformable shapes. If the surfaces have the same intrinsic shapes, their deformations can be described accurately by a conformal model. The uniformization theorem allows the shapes to be conformally mapped onto a canonical domain, wherein the shapes can be aligned using a M¨obius transformation. The problem of correspondence-free M¨obius alignment of two sets of noisy and partially overlapping point sets can be tackled as a maximum consensus problem. Solving for the M¨obius transformation can be approached by randomized voting-type methods which offers no guarantee of optimality. Local methods such as Iterative Closest Point can be applied, but with the assumption that a good initialization is given or these techniques may converge to a bad local minima. When a globally optimal solution is required, the literature has so far considered only brute-force search. This thesis contributes a new branch-and-bound algorithm that solves for the globally optimal M¨obius transformation much more efficiently. So far, the consensus maximization problems are approached mainly by randomized algorithms, which are efficient but offer no analytical convergence guarantee. On the other hand, there exist exact algorithms that can solve the problem up to global optimality. The global methods, however, are intractable in general due to the NP-hardness of the consensus maximization. To fill the gap between the two extremes, this thesis contributes two novel deterministic algorithms to approximately optimize the maximum consensus criterion. The first method is based on non-smooth penalization supported by a Frank-Wolfe-style optimization scheme, and another algorithm is based on Alternating Direction Method of Multipliers (ADMM). Both of the proposed methods are capable of handling the non-linear geometric residuals commonly used in computer vision. As will be demonstrated, our proposed methods consistently outperform other heuristics and approximate methods.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 201

Prediction of Visual Behaviour in Immersive Contents

In the world of broadcasting and streaming, multi-view video provides the ability to present multiple perspectives of the same video sequence, therefore providing to the viewer a sense of immersion in the real-world scene. It can be compared to VR and 360° video, still, there are significant differences, notably in the way that images are acquired: instead of placing the user at the center, presenting the scene around the user in a 360° circle, it uses multiple cameras placed in a 360° circle around the real-world scene of interest, capturing all of the possible perspectives of that scene. Additionally, in relation to VR, it uses natural video sequences and displays. One issue which plagues content streaming of all kinds is the bandwidth requirement which, particularly on VR and multi-view applications, translates into an increase of the required data transmission rate. A possible solution to lower the required bandwidth, would be to limit the number of views to be streamed fully, focusing on those surrounding the area at which the user is keeping his sight. This is proposed by SmoothMV, a multi-view system that uses a non-intrusive head tracking approach to enhance navigation and Quality of Experience (QoE) of the viewer. This system relies on a novel "Hot&Cold" matrix concept to translate head positioning data into viewing angle selections. The main goal of this dissertation focus on the transformation and storage of the data acquired using SmoothMV into datasets. These will be used as training data for a proposed Neural Network, fully integrated within SmoothMV, with the purpose of predicting the interest points on the screen of the users during the playback of multi-view content. The goal behind this effort is to predict possible viewing interests from the user in the near future and optimize bandwidth usage through buffering of adjacent views which could possibly be requested by the user. After concluding the development of this dataset, work in this dissertation will focus on the formulation of a solution to present generated heatmaps of the most viewed areas per video, previously captured using SmoothMV