In this paper, we contrive a stereo matching algorithm with careful handling of disparity, discontinuity and occlusion. This algorithm works a worldwide matching stereo model which is based on minimization of energy. The global energy comprises two terms, firstly the data term and secondly the smoothness term. The data term is approximated by a color-weighted correlation, then refined in obstru...

Several algorithms are common for estimating depth from stereo series, but many of them have difficulties when determining depth of objects having periodical structure. This contribution proposes a method to overcome the impediments by using defocus as additional information. The algorithm fuses depth from stereo and depth from defocused edges by analyzing and evaluating image series with simul...

Absolute range detection has been a long-time objective of studies focused on achieving stereo computer vision. Many researchers have attempted to emulate some part of the human stereo system, most commonly motion parallax. These attempts, however, have concluded that motion parallax is not a reliable source of input for systems attempting range measurements and, in fact, often worsens object s...

The performance of depth reconstruction in binocular stereo relies on how adequate the predefined baseline for a target scene is. Wide-baseline stereo is capable of discriminating depth better than the narrow-baseline stereo, but it often suffers from spatial artifacts. Narrow-baseline stereo can provide a more elaborate depth map with fewer artifacts, while its depth resolution tends to be bia...

Recovering the 3D structure of the scene from images yields useful information for tasks such as shape and scene recognition, object detection, or motion planning and object grasping in robotics. In this thesis, we introduce a general machine learning approach called unsupervised CRF learning based on maximizing the conditional likelihood. We describe the application of our machine learning app...

Area-based stereo algorithms, as the most universal of all stereovision methods, have the potential to become widely used in many industrial sectors, but their relatively low speed halt the usage. This paper describes the method which can significantly increase computing speed. This new method is called false epipolar constraint. It is an addition to epipolar constraint which can reduce the set...

Depth information can be of great value in the analysis of scenes of three-dimensional objects. A stereo pair of images allows extraction of such depth information. Use of a number of progressive, closely spaced views has the advantage of reliability over the use of just two views for stereo, and potential savings of computational effort. Such progressive views are naturally available in many a...

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 performe...

Many fundamental computer vision problems, including optical flow estimation and stereo matching, involve the key step of computing dense color matching among pixels. In this paper, we show that by merely upsampling, we can improve sub-pixel correspondence estimation. In addition, we identify the regularization bias problem and explore its relationship to image resolution. We propose a general ...

When a robot moves about a 2D world such as a planar surface, it is important that obstacles to the robot's motions be detected. This classical problem of \obstacle detection" has proven to be di cult. Many researchers have formulated this problem as being the process of determining where a robot cannot move due to the presence of obstacles. An alternative approach presented here is to determin...