Only few problems in computer vision have been investigated more vigorously than stereo. Nevertheless, the main obstacle on the way to their practical application is the excessively long computation time needed to match stereo images. This paper presents parallel algorithms for edge-based stereo suitable for depth computation. Edge-based stereo techniques produce only sparse depth maps. Thus, w...

The history of stereo analysis of images dates back more than one hundred years, but stereo analysis of image sequences is a fairly recent subject. Sequences allow time-propagation of results, but also come with particular characteristics such as being of lower resolution, or with less contrast. This article discusses the application of belief propagation (BP), which is widely used for solving ...

Robot vision tasks often use stereo methods to obtain 3-d information on the environment. This paper shows how color information can be utilized to improve the results of dense stereo matching as well as the results of edge-based stereo matching. A hierarchical chromatic Block Matching technique to obtain dense stereo correspondence is presented. The results have been compared to the results of...

Mankind’s fascination with three-dimensional images and stereo vision can be traced back to the ancient Greeks, and it was photography and the invention of the stereoscope during the mid-nineteenth century that helped to spur large-scale public interest in stereo images and stereo viewing techniques. Stereoscopes made it possible to see a pair of slightly different two-dimensional photographs i...

In this paper, we propose an efficient stereo image rectification method using the horizontal baseline. Since the stereo camera is generally manually arranged, there are geometric errors due to the camera misalignment and the differences between the camera internal characteristics. Although the conventional calibration-based stereo image rectification method is simple, it has an opportunity to ...

This paper presents a new method for learning overcomplete dictionaries adapted to efficient joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms of dictionary atoms in two stereo views. A maximum-likelihood method for learning stereo dictionaries is then proposed, which includes a multi...

In traditional stereoscopic displays, the virtual three-dimensional object does not appear to be fixed in space as the viewer's head moves. This apparent motion results from the fact that a correct stereo image can only be formed for a particular viewpoint and interpupillary distance. At other viewpoints, our brain interprets the stereo image as a slightly skewed and rotated version of the orig...

We describe the use of a deep learning method for semantic segmentation of the liver from color images. Our intent is to eventually embed a semantic segmentation method into a stereo-vision based navigation system for open liver surgery. Semantic segmentation of the stereo images will allow us to reconstruct a point cloud containing the liver surfaces and excluding all other non-liver structure...