Performance Analysis on Stereo Matching Algorithms Based on Local and Global Methods for 3D Images Application

Stereo matching is one of the methods in computer vision and image processing. There have numerous algorithms that have been found associated between disparity maps and ground truth data. Stereo Matching Algorithms were applied to obtain high accuracy of the depth as well as reducing the computational cost of the stereo image or video. The smoother the disparity depth map, the better results of triangulation can be achieved. The selection of an appropriate set of stereo data is very important because these stereo pairs have different characteristics. This paper discussed the performance analysis on stereo matching algorithm through Peak Signal to Noise Ratio (PSNR in dB), Structural Similarity (SSIM), the effect of window size and execution time for different type of techniques such as Sum Absolute Differences (SAD), Sum Square Differences (SSD), Normalized Cross Correlation (NCC), Block Matching (BM), Global Error Energy Minimization by Smoothing Functions, Adapting BP and Dynamic Programming (DP). The dataset of stereo images that used for the experimental purpose is obtained from Middlebury Stereo Datasets

Development of Double Stage Filter (DSF) for Stereo Matching Algorithms and 3D Vision Applications

A part of the stereo matching algorithms development is mainly focused on overcoming unwanted aspects such as noises, unwanted regions and occlusions. In this paper, a new technique which is called Double Stage Filter (DSF) is introduced. This technique is a hybrid algorithm which consists of dynamic programming and block matching. The main feature of DSF is mainly its function at the post-processing stage that is to remove the noises and horizontal stripes, obtained from the raw disparity depth map of dynamic programming. In order to remove the unwanted aspects, a two-stage filtering process is applied. In this DSF algorithm, segmentation process is also required to segment the optimized raw disparity depth map into several parts according to the pixel colours. The first filter block is applied to remove the noises of the segmented parts before merging. Meanwhile, the second filter is used to remove the unwanted region of the outliers on segmented parts after merging processes. The new disparity depth map of DSF is evaluated in Middlebury Stereo Vision page with a few evaluation functions, such as similarity structural (SSIM), peak to signal noise ratio (PSNR) and mean square errors (MSE). At the end of this paper, the performance of DSF is compared with other techniques

Low-cost volume estimation by two-view acquisitions: a computational intelligence approach

The estimation of the volume occupied by an object is an important task in the fields of granulometry, quality control, and archaeology. An accurate and well know technique for the volume measurement is based on the Archimedes' principle. However, in many applications it is not possible to use this technique and faster contact-less techniques based on image processing or laser scanning should be adopted. In this work, we propose a low-cost approach for the volume estimation of different kinds of objects by using a two-view vision approach. The method first computes a reduced threedimensional model from a single couple of images, then extracts a series of features from the obtained model. Lastly, the features are processed using a computational intelligence approach, which is able to learn the relation between the features and the volume of the captured object, in order to estimate the volume independently of its position and angle, and without computing a full three-dimensional model. Results show that the approach is feasible and can obtain an accurate volume estimation. Compared to the direct computation of the volume from the three-dimensional models, the approach is more accurate and also less dependent to the position and angle of the measured objects with respect to the cameras

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

Traitement et analyse d'images stéréoscopiques avec les approches du calcul générique sur un processeur graphique

RÉSUMÉ Étant donné une paire d'images stéréoscopiques, il est possible de calculer une carte de disparité dense qui encode les correspondances par pixel entre deux vues d'une même scène. Étant donné les paramètres de calibration d'une paire d'appareils photo, il est possible de transformer une carte de disparités en une carte de profondeur. Il existe de nombreuses applications allant de la robotique et de l'interaction humain-machine à la photographie 3D qui peuvent bénéficier de l'utilisation de cartes de disparité précises. Nous nous intéressons à la production de cartes de disparité de haute qualité à partir d'images stéréo pour des applications à temps réel ou dans un ordre de grandeur de temps réel par rapport au nombre d'images par seconde pour des vidéos typiques si un traitement hors ligne est acceptable. Nous avons donc étudié de possibilités d'accélérer de divers calculs clés nécessaires pour produire des cartes de disparité à partir d'images stéréoscopiques. Tout d'abord, nous explorons le potentiel de détecter les disparités incompatibles avec un calcul rapide sur les images en basse définition et d'une vérification de consistance basée sur la comparaison entre une paire de cartes de disparité de gauche à droite et de droite à gauche. L'idée est que les disparités incompatibles sont susceptibles de contenir des erreurs. Puis nous évaluons le potentiel d'appliquer de calculs sélectifs en employant les images stéréoscopiques de plus haute définition afin de réduire les erreurs tout en évitant de calculs coûteux sur les images stéréoscopiques en plus haute définition tout entières. Nous avons aussi introduit une méthode d'interpolation simple et rapide qui est capable d'améliorer la qualité d'une carte de disparité si la densité de pixels consistants est élevée. Des travaux récents ont montré que la qualité d'une carte de disparité peut être améliorée en combinant différentes mesures de distance. Nous explorons une fonction de combinaison simple pour la somme des différences au carré et les distances de Hamming entre les blocs d'image représentés par la transformation Census. Nous montrons que cette technique de combinaison peut produire d'améliorations significatives quant à la qualité de carte de disparité. Nous explorons aussi des approches fondées sur la combinaison des deux mesures et la combinaison d'utilisation d'imageries en haute et basse résolutions de manière sélective. Nous montrons aussi comment l'essence de méthodes populaires et d'état de l'art d'inférence semi-globale peut être formulée en utilisant des modèles de Markov cachés. Cela nous permet de généraliser les approches semi-globales à des modèles plus sophistiqués tout en ouvrant la porte aux paramètres des modèles d'apprentissage en utilisant des techniques du maximum de vraisemblance. Pour accélérer les calculs, normalement nous avons employé le calcul générique sur un processeur graphique (GPGPU). En particulier, nous avons implémenté en OpenCL une variation de la mise en correspondance par bloc basée sur la somme des différences au carré et présenté une version corrigée de l'implémentation de l'algorithme Viterbi en OpenCL qui était fournie dans un kit de développement logiciel de GPU. ----------ABSTRACT Given a pair of stereo images it is possible to compute a dense disparity map which encodes the per pixel correspondences between views. Given calibrated cameras it is possible to transform a disparity map into a depth map. There are many applications ranging from robotics and human computer interaction to 3D photography that benefit from the use of precise disparity maps. We are interested in producing high quality disparity maps from stereo imagery as quickly as possible for real-time applications or within an order of magnitude of real-time for typical video rates for applications where off-line processing is acceptable. We therefore explore the problem of accelerating various key computations needed to produce disparity maps from stereo imagery. First, we explore the potential of detecting inconsistent disparities with fast but low resolution comparisons and a consistency check based on comparing left to right and right to left disparity maps. The idea is that inconsistent disparities are likely to contain errors. We then evaluate the potential of selectively applying computation using higher resolution imagery in order to reduce errors while avoiding expensive computations over the entire high resolution image. We also introduce a simple and fast interpolation method that is capable of improving the quality of a disparity map if the density of consistent pixels is high. Recent work has shown that disparity map quality can be also be improved by combining different distance metrics. We explore a simple combination function for sum of squared difference and Hamming distances between image blocks represented using the Census transform. We show that this combination technique can produce significant improvements in disparity map quality. We also explore approaches based on both combining metrics and selectively combining high and low resolution imagery. We also show how the essence of popular, state of the art semi-global inference methods can be formulated using hidden Markov models. This allows us to generalize semi-global approaches to more sophisticated models while also opening the door to learning model parameters using maximum likelihood techniques. To accelerate computations generally we use general purpose graphical processing unit (GPGPU) computing. In particular, we have implemented a variation of sum of squared difference block matching in OpenCL and present a corrected version of an OpenCL Viterbi algorithm implementation that was provided in a GPU software development kit

People Detection and Tracking Based on Stereovision and Kalman Filter

[ES] Los sistemas de conteo de personas son extensamente utilizados en aplicaciones de vigilancia. En este artículo se presenta una aplicación para realizar conteo de personas a través de un sistema de estereovisión. Este sistema obtiene tasas de conteo de las personas en movimiento que atraviesan la zona de conteo recogida por el sistema estéreo distinguiendo entrada y salida. Para realizar este conteo se precisan dos fases fundamentales: detección y seguimiento. La detección se basa en la búsqueda de las cabezas de las personas por medio de una correlación de la imagen preprocesada con distintos patrones circulares, filtrando dichas detecciones por estereovisión en función de la altura. El seguimiento se lleva a cabo mediante una algoritmo de múltiples hipótesis basado en filtro de Kalman. Por último, se realiza el conteo según el camino seguido por las trayectorias. Se ha experimentado con un conjunto de vídeos reales tomados en distintas zonas de tránsito en interiores de edificios, alcanzando tasas que oscilan entre un 87% y un 98% de acierto según la cantidad de flujo de personas que atraviesan la zona de conteo de forma simultánea. En los distintos vídeos utilizados como prueba se han reproducido todo tipo de situaciones adversas, como oclusiones, personas en grupo en diferentes sentidos, cambios de iluminación, etc.[EN] The people counting systems are widely used in surveillance applications. This article presents an application for counting people through a stereovision system. This system obtains counting rates of people moving through the counting area, distinguishing between input and output. To achieve this aim is required two basic steps: detection and tracking. The detection step is based on correlation through a pre-processed image with various circular patterns in order to search people's heads, filtering these detections by stereovision depending on the height. The people tracking is carried out through a multiple hypothesis algorithm based on the Kalman filter. Finally, people counting is done according to the trajectory followed by the person. To validate the algorithm have been used several real videos taken from different transit areas inside buildings, reaching rates ranging between 87% and 98% accuracy depending on the number of people crossing the counting zone simultaneously. In these videos occur several adverse situations, such as occlusions, people in groups in different directions, lighting changes, etc.Este trabajo ha sido realizado gracias al Programa Nacional de Diseño y Producción Industrial del Ministerio de Ciencia y Tecnología, a través del proyecto ESPIRA (ref. DPI2009-10143) y a la Universidad de Alcalá (ref.UAH2011/EXP-001), a través del proyecto ”Sistema de Arrays de Cámaras Inteligentes (SACI)”.García, J.; Gardel, A.; Bravo, I.; Lázaro, JL.; Martínez, M.; Rodríguez, D. (2012). Detección y Seguimiento de Personas Basado en Estereovisión y Filtro de Kalman. Revista Iberoamericana de Automática e Informática industrial. 9(4):453-461. https://doi.org/10.1016/j.riai.2012.09.012OJS45346194Donate, A., Liu, X., & Collins, E. G. (2011). Efficient Path-Based Stereo Matching With Subpixel Accuracy. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(1), 183-195. doi:10.1109/tsmcb.2010.2049839Englebienne, G., van Oosterhout, T., Krose, B., 2009. Tracking in sparse multi- camera setups using stereo vision. In: Proc. Third ACM/IEEE Int. Conf. Distributed Smart Cameras ICDSC 2009. pp. 1-6.Mucientes, M., Burgard, W., oct. (2006). Multiple hypothesis tracking of clusters of people. In: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on. pp. 692-697.Rizzon, L., Massari, N., Gottardi, M., Gasparini, L., 2009. A low-power people counting system based on a vision sensor working on contrast. In: Proc. IEEE Int. Symp. Circuits and Systems ISCAS 2009.Xu, H., Lv, P., Meng, L., 2010. A people counting system based on head- shoulder detection and tracking in surveillance video. In: Proc. Int Computer Design and Applications (ICCDA) Conf. Vol. 1