Modèle a contrario pour la mise en correspondance robuste sous contraintes épipolaires et photométriques

National audienceLa mise en correspondance de points d'intérêt entre deux vues est une des étapes clés en vision par ordinateur, en particulier dans l'analyse de la structure et du mouvement. Après l'extraction de points d'intérêt, deux étapes sont généralement mises en oeuvre : la mise en correspondance de ceux-ci en gardant les "meilleurs appariements" selon une mesure de ressemblance photométrique adaptée, puis la sélection des appariements cohérents avec la géométrie induite par le mouvement de la caméra. La présence de motifs répétés, ou des forts changements de point de vue peuvent générer de nombreux appariements aberrants. Nous présentons une méthode a contrario étendant celle de Moisan et Stival, qui regroupe ces deux étapes. L'approche proposée ne nécessite pas de paramètre critique et permet un gain significatif en nombre d'appariements obtenus et en précision, en particulier en présence de motifs répétés ou de forts changements des points de vue

Estimating Confidences for Classifier Decisions using Extreme Value Theory

Classifiers generally lack a mechanism to compute decision confidences. As humans, when we sense that the confidence for a decision is low, we either conduct additional actions to improve our confidence or dismiss the decision. While this reasoning is natural to us, it is currently missing in most common decision algorithms (i.e., classifiers) used in computer vision or machine learning. This limits the capability for a machine to take further actions to either improve a result or dismiss the decision. In this thesis, we design algorithms for estimating the confidence for decisions made by classifiers such as nearest-neighbor or support vector machines. We developed these algorithms leveraging the theory of extreme values. We use the statistical models that this theory provides for modeling the classifier's decision scores for correct and incorrect outcomes. Our proposed algorithms exploit these statistical models in order to compute a correctness belief: the probability that the classifier's decision is correct. In this work, we show how these beliefs can be used to filter bad classifications and to speed up robust estimations via sample and consensus algorithms, which are used in computer vision for estimating camera motions and for reconstructing the scene's 3D structure. Moreover, we show how these beliefs improve the classification accuracy of one-class support vector machines. In conclusion, we show that extreme value theory leads to powerful mechanisms that can predict the correctness of a classifier's decision

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

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

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

Técnicas de fotogrametría y visión por computador para el modelado 3D de estructuras geomorfológicas dinámicas

Los diferentes enfoques que tienen la visión por computador y la fotogrametría de acercarse al problema del modelado y medida tridimensionales (3D) aportan un interesante punto de partida para esta tesis doctoral. Son disciplinas con objetivos similares (la construcción de modelos 3D de objetos reales, entre otros), pero provienen de mundos dispares y la forma de acercarse al problema es diferente y a la vez complementaria. En esta tesis se pretende combinar técnicas desarrolladas en el campo de la visión por computador y de la fotogrametría para desarrollar métodos automáticos que nos permitan generar productos cartográficos tridimensionales de estructuras dinámicas. Aunque las técnicas que se desarrollen durante este trabajo pretenden ser generales en cuanto a su campo de aplicación, nos centraremos principalmente en un caso de especial relevancia. En concreto se trabajará con estructuras geomorfológicas dinámicas (glaciares rocosos), que conllevan una serie de características especiales que dificultan el uso de técnicas convencionales y son de gran interés actual en el ámbito de la geomorfología, entre otras cosas por permitir analizar efectos del “cambio climático”.Computer Vision and Photogrammetry try to solve the problem of 3D reconstruction in a different way and that is the interesting beggining of this thesis project. These disciplines have similar objective (3D reconstruction of real objects), but come from different fields and have very different manners to approach the problem. The goal of this project is to use known Computer Vision and Photogrammetry techniques and develope new automatic methods to produce 3D cartography products of dynamic structures. Although it is very important get techniques with a general purpose, during thesis development we work with dynamic geomorphologic structures (rock glaciers), which a set of special features that make it interesting like field of application. Rock glacier studies are very interesting because allow experts analyze climate change and his effects.Junta de Extremadura: Beca predoctoral. Ministerio de Educación y Ciencia: Proyecto de investigación (CGL2007-65295/BTE). Ministerio de Medio Ambiente: Proyecto (007-2007. Ministerio de Ciencia e Innovación: Proyecto de investigación (GL2010-19729). Ministerio de Medio Ambiente, Rural y Marino: Proyecto de investigación (OAPN053-2010). Ministerio de Medio Ambiente: Proyecto de investigación (018-2007), acogido al Grupo de investigación 2009SGR0868

Error Detection, Factorization and Correction for Multi-View Scene Reconstruction from Aerial Imagery

Scene reconstruction from video sequences has become a prominent computer vision research area in recent years, due to its large number of applications in fields such as security, robotics and virtual reality. Despite recent progress in this field, there are still a number of issues that manifest as incomplete, incorrect or computationally-expensive reconstructions. The engine behind achieving reconstruction is the matching of features between images, where common conditions such as occlusions, lighting changes and texture-less regions can all affect matching accuracy. Subsequent processes that rely on matching accuracy, such as camera parameter estimation, structure computation and non-linear parameter optimization, are also vulnerable to additional sources of error, such as degeneracies and mathematical instability. Detection and correction of errors, along with robustness in parameter solvers, are a must in order to achieve a very accurate final scene reconstruction. However, error detection is in general difficult due to the lack of ground-truth information about the given scene, such as the absolute position of scene points or GPS/IMU coordinates for the camera(s) viewing the scene. In this dissertation, methods are presented for the detection, factorization and correction of error sources present in all stages of a scene reconstruction pipeline from video, in the absence of ground-truth knowledge. Two main applications are discussed. The first set of algorithms derive total structural error measurements after an initial scene structure computation and factorize errors into those related to the underlying feature matching process and those related to camera parameter estimation. A brute-force local correction of inaccurate feature matches is presented, as well as an improved conditioning scheme for non-linear parameter optimization which applies weights on input parameters in proportion to estimated camera parameter errors. Another application is in reconstruction pre-processing, where an algorithm detects and discards frames that would lead to inaccurate feature matching, camera pose estimation degeneracies or mathematical instability in structure computation based on a residual error comparison between two different match motion models. The presented algorithms were designed for aerial video but have been proven to work across different scene types and camera motions, and for both real and synthetic scenes