Efficient Stereo Matching with Decoupled Dissimilarity Measure Using Successive Weighted Summation

Developing matching algorithms from stereo image pairs to obtain correct disparity maps for 3D reconstruction has been the focus of intensive research. A constant computational complexity algorithm to calculate dissimilarity aggregation in assessing disparity based on separable successive weighted summation (SWS) among horizontal and vertical directions was proposed but still not satisfactory. This paper presents a novel method which enables decoupled dissimilarity measure in the aggregation, further improving the accuracy and robustness of stereo correspondence. The aggregated cost is also used to refine disparities based on a local curve-fitting procedure. According to our experimental results on Middlebury benchmark evaluation, the proposed approach has comparable performance when compared with the selected state-of-the-art algorithms and has the lowest mismatch rate. Besides, the refinement procedure is shown to be capable of preserving object boundaries and depth discontinuities while smoothing out disparity maps

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion

Detection of dominant planar surfaces in disparity images based on random sampling

U ovom članku ispituje se praktična primjenjivost RANSAC-pristupa za detekciju ravnih površina na slikama dispariteta dobivenim pomoću stereo vizije. Težište istraživanja je primjena u interijerima, gdje je velik dio dominantnih površina jednolično obojen, što predstavlja poseban problem za stereo viziju. Ispitano je nekoliko jednostavnih modifikacija osnovnog RANSAC-algoritma s ciljem utvrđivanja koliko oni mogu poboljšati njegovu učinkovitost. Predložene su dvije jednostavne mjere točnosti rekonstrukcija ravnih površina. Provedeno je eksperimentalno istraživanje na slikama snimljenim sustavom stereo vizije montiranom na mobilnog robota koji se kretao hodnicima fakulteta.In this paper, the applicability of RANSAC-approach to planar surface detection in disparity images obtained by stereo vision is investigated. This study is specially focused on application in indoor environments, where many of the dominant surfaces are uniformly colored, which poses additional difficulties to stereo vision. Several simple modifications to the basic RANSAC-algorithm are examined and improvements achieved by these modifications are evaluated. Two simple performance measures for evaluating the accuracy of planar surface detection are proposed. An experimental study is performed using images acquired by a stereo vision system mounted on a mobile robot moving in an indoor environment

Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels

Bódis-Szomorú A., Riemenschneider H., Van Gool L., ''Fast, approximate piecewise-planar modeling based on sparse structure-from-motion and superpixels'', 27th IEEE conference on computer vision and pattern recognition - CVPR 2014, pp. 469-476, June 23-28, 2014, Columbus, Ohio, USA.status: publishe