A statistical representation of three-dimensional shapes is introduced, based on a novel four-dimensional feature. The feature parameterizes the intrinsic geometrical relation of an oriented surface-point pair. The set of all such features represents both local and global characteristics of the surface. We compress this set into a histogram. A database of histograms, one per object, is sampled in a training phase. During recognition, sensed surface data, as may be acquired by stereo vision, a laser range-scanner, etc., are processed and compared to the stored histograms. We evaluate the match quality by six different criteria that are commonly used in statistical settings. Experiments with artificial data containing varying levels of noise and occlusion of the objects show that Kullback-Leibler and likelihood matching yield robust recognition rates. The present study proposes histograms of the geometric relation between two oriented surface points (surflets) as a compact yet distinctive representation of arbitrary three-dimensional shapes.

In order to improve the performance of correlation-based disparity computation of stereo vision algorithms, standard methods need to choose in advance the value of the maximum disparity (MD). This value corresponds to the maximum displacement of the projection of a physical point expected between the two images. It generally depends on the motion model, the camera intrinsic parameters and on the depths of the observed scene.In this paper, we show that there is no optimal MD value that minimizes the matching errors in all image regions simultaneously and we propose a novel approach of the disparity computation that does not rely on any a priori MD. Two variants of this approach will be presented. When compared to traditional correlation-based methods, we show that our approach improves not only the accuracy of the results but also the efficiency of the algorithm. A local energy minimization is also proposed for fast refinement of the results.An extensive comparative study with ground truth is carried out on classical stereo images and the results show that the proposed method clearly gives more accurate results and it is two times faster than the fastest possible implementation of traditional correlation-based methods.

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Abstract-In vehicular applications based on motion-stereo using monocular side-looking cameras, pairs of images must usually be rectified very well, to allow the application of dense stereo methods. But also long-term installations of stereo rigs in vehicles require approaches that cope with the decalibration of the cameras. The need for such methods is further underlined by the fact that offline camera calibration is a costly and timeconsuming procedure at vehicle production sites.In this paper we propose an approach for dense stereo matching that overcomes issues arising from inaccurately rectified images. For this, we significantly increase the search range for correspondences, but still preserve a high efficiency of the method to allow operation on platforms with highly limited processing resources.We demonstrate the performance of our ideas quantitatively using well known stereo datasets and qualitatively using real video sequences of a motion-stereo application.

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Abstract. In this paper we address the problem of dense stereo matching and computation of optical flow. We propose a generalized dense correspondence computation algorithm, so that stereo matching and optical flow can be performed robustly and efficiently at the same time. We particularly target automotive applications and tested our method on real sequences from cameras mounted on vehicles. We performed an extensive evaluation of our method using different similarity measures and focused mainly on difficult real-world sequences with abrupt exposure changes. We did also evaluations on Middlebury data sets and provide many qualitative results on real images, some of which are provided by the adverse vision conditions challenge of the conference.