This paper presents fast algorithms for similarity measure, stereo matching, panoramic stereo matching, and image motion estimation. The disparity map for the stereo images is found in the 3D correlation coefficient volume by obtaining the global 3D maximumsurface by using our two-stage dynamic programming (TSDP) technique. Fast panoramic stereo matching is carried out using a cylindrical maxim...

Stereo-vision in computer vision represents an important field for 3D reconstruction. Real time dense reconstruction, however, is only achieved for high textured surfaces in passive stereo-matching. In this work an active stereo-matching approach is proposed. A projected pattern is used to artificially increase the texture of the measuring object, thus enabling dense reconstruction for one-shot...

Stereo vision is the recovery of depth of a scene using multiple images with di erent points of view. The major task in stereo vision is establishing correspondence of points between images given a known pinhole-model of the stereo cameras. Two major approaches to matching points have been used widely: feature-based matching and areaor correlationbased matching. It is well known that both appro...

Stereo matching algorithms and multi camera reconstruction algorithms are usually compared using benchmarks. These benchmarks compare the quality of the resulting depth map or reconstructed surface mesh. We describe the differences between several known stereo and multi-view stereo benchmarks and their various datasets. Also the modifications that are necessary to use our own GPU based multi ca...

The reduction of the search region in stereo correspondence can improve the performance of the matching process in terms of execution time and accuracy. For the edge-based stereo matching, we establish the relationship between a search space and parameters such as relative displacement of the edges, the disparity under consideration, the image resolution, the CCD dimension, and the focal length...

Most of the existing stereo matching algorithms assume that the corresponding pixels have the same intensity (color) in both images. But in real world situations, image color values are often affected by various radiometric factors such as illumination direction, illuminant color, and imaging device changes. Hence, we cannot fully depend on the raw color recorded by a camera. So, the assumption...

In this paper, we propose a hierarchical framework for stereo matching. Similar to the conventional image pyramids, a series of images with less and less information is constructed. The objective is to use bit-plane slicing technique to investigate the feasibility of correspondence matching with less bits of intensity information. In the experiments, stereo matching with various bit-rate image ...

In the context of computer vision, stereo matching can be done using correlation measures. Few papers deal with color correlationbased matching so the underlying problem of this paper is about how it can be adapted to color images. The goals of this work are to help choosing a color space and to generalize the correlation measures to color. Nine color spaces and three different methods have bee...

We propose a new approach for stereo matching in Autonomous Mobile Robot applications. In this framework an accurate but slow reconstruction of the 3D scene is not needed; rather, it is more important to have a fast localization of the obstacles to avoid them. All the methods in the literature are based on a punctual correspondence, but they are inefficient in realistic contexts for the presenc...

The System-on-Programmable-Chip (SoPC) architecture to implement a stereo matching algorithm based on the sum of absolute differences (SAD) in a FPGA chip is proposed. The hardware implementation involves a 32-bit Nios II microprocessor, memory interfaces and stereo matching algorithm circuit module. The Nios II microprocessor is a configurable soft IP core in charge of managing the buffer of t...