Performance Analysis between Basic Block Matching and Dynamic Programming of Stereo Matching Algorithm

One of the most important key steps of stereo vision algorithms is the disparity map implementation, where it generally utilized to decorrelate data and recover 3D scene framework of stereo image pairs. However, less accuracy of attaining the disparity map is one of the challenging problems on stereo vision approach. Thus, various methods of stereo matching algorithms have been developed and widely investigated for implementing the disparity map of stereo image pairs including the Dynamic Programming (DP) and the Basic Block Matching (BBM) methods. This paper mainly presents an evaluation between the Dynamic Programming (DP) and the Basic Block Matching (BBM) methods of stereo matching algorithms in term of disparity map accuracy, noise enhancement, and smoothness. Where the Basic Block Matching (BBM) is using the Sum of Absolute Difference (SAD) method in this research as a basic algorithm to determine the correspondence points between the target and reference images. In contrast, Dynamic Programming (DP) has been used as a global optimization approach. Besides, there will be a performance analysis including graphs results from both methods presented in this paper, which can show that both methods can be used on many stereo vision applications

Fast stereo matching using reliability-based dynamic programming and consistency constraints

A method for solving binocular and multi-view stereo matching problems is presented in this paper. A weak consistency constraint is proposed, which expresses the visibility constraint in the image space. It can be proved that the weak consistency constraint holds for scenes that can be represented by a set of 3D points. As well, also proposed is a new reliability measure for dynamic programming techniques, which evaluates the reliability of a given match. A novel reliability-based dynamic programming algorithm is derived accordingly, which can selectively assign disparity values to pixels when the reliabilities of the corresponding matches exceed a given threshold. Consistency constraints and the new reliabilitybased dynamic programming algorithm can be combined in an iterative approach. The experimental results show that the iterative approach can produce dense (60~90%) and reliable (total error rate of 0.1~1.1%) matching for binocular stereo datasets. It can also generate promising disparity maps for trinocular and multi-view stereo datasets.

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Mise en correspondance de pixels pour la stéréovision binoculaire par propagation d'appariements de points d'intérêt et sondage de régions

La mise en correspondance est un des principaux problèmes de la vision par ordinateur qui consiste à trouver dans 2 images d'une même scène, les couples de pixels qui sont les projections d'un même point. Une contribution de ce travail porte sur un type particulier de méthode : propagation de germes. La zone de recherche des correspondants est réduite aux voisinages d'appariements fiables (germes), en faisant l'hypothèse que, 2 pixels voisins ont des correspondants proches. Cependant, le succès de ce type de méthode est dépendant du choix des germes. Nous proposons une étude de l'étape de sélection des germes. Nous nous concentrons sur la mise en correspondance de points d'intérêt. Nous avons besoin de pixels qui peuvent être mis en correspondance de manière sûre. Nous comparons 14 détecteurs associés à 5 mesures de corrélation. Certaines de ces mesures sont conçues pour être robustes à un des principaux problèmes de la mise en correspondance stéréo : les ruptures de profondeur. Cette étude donne des conseils sur la manière de choisir les paramètres des différentes méthodes afin de trouver les meilleurs germes possibles. Ensuite, ces germes sont utilisés avec 2 approches de propagation et les résultats sont évalués. Une autre contribution porte sur une nouvelle approche de mise en correspondance dense fondée régions. Différentes segmentations couleur sont utilisées. Plusieurs instances d'un modèle de surface sont calculées pour les différentes régions selon des disparités initiales tirées au sort. Pour chaque pixel, chaque instance donne une disparité qui est considérée comme un vote. La disparité qui reçoit le plus de voix est sélectionnée comme disparité finale.Stereo matching is one of the main topics in computer vision. It consists in finding in 2 images of a same scene the pairs of pixels which are the projections of a same scene point. A contribution of this thesis deals with a special kind of local method called seeds propagation. The search area of a correspondent is reduced to the neighbourhoods of reliable matches called seeds. However, the success of such a method depends on the choice of these seeds. In this dissertation, we give a study of the seeds selection step. We focus on feature points matching. These are special points in the image with interesting characteristics for a given application. In our case, we need pixels that can be matched with high confidence. We compare 14 different detectors linked to five correlation measures. Some of these measures are meant to be robust to one of the main challenge in stereo matching: depth discontinuities. Besides, this study gives advice on the choice of the parameters of the different techniques to be able to find the best solutions according some given criteria. Then, these seeds are used with two approaches of propagation and the results are evaluated. Another contribution deals with a new region-based approach for dense stereo matching. Different colour segmentations are used. Then, many instances of a surface model are computed for the different regions according to initial disparities selected randomly. For each pixel, each instance gives a disparity value regarded as a vote. Finally, the most voted value is selected as the final disparity. This approach is relatively easy to implement and very effective giving competitive results among the state of the art

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available