Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes. The techniques are essentially complementary: DfD can robustly handle repetitive textures that are problematic for stereo matching whereas stereo matching is insensitive to defocus blurs and can handle large depth range. In this paper, we present a unified learning-based technique to conduct hybrid ...

Extraction of three-dimensional scene from the stereo images is the most effective research area in the field of computer vision. Stereo vision constructs the actual three-dimensional scene from two stereo images having different viewpoints. Stereo matching is a correspondence problem, that means it ascertains which part of image corresponds to which part of another image ,where variations insi...

The stereo matching aims to find corresponding entities between two (or more) images, i.e. entities that are projections of the same 3D object in the scene. Constraints used in stereo matching can be classified into two categories: local constraints, which rely only on a pixel and on some pixels in its surrounding, and global constraints, which must be verified by the whole pixels of a line or ...

An important research topic in image processing is stereo vision. The objective is to compute a 3-dimensional representation of some scenery from two 2-dimensional digital images. Constructing a 3-dimensional representation involves finding pairs of pixels from the two images which correspond to the same point in space. Several stereo matching algorithms are developed to find matching pairs. Hi...

In this paper, we propose an efficient framework for edge-preserving stereo matching. Local methods for stereo matching are more suitable than global methods for real-time applications. Moreover, we can obtain accurate depth maps by using edge-preserving filter for the cost aggregation process in local stereo matching. The computational cost is high, since we must perform the filter for every n...

A parallel stereo matching algorithm is presented which is mainly thought for the processing of images from pushbroom stereo cameras. The algorithm is designed for non-epipolar geometry, because of disturbances of flight attitude and velocity. Existing epipolar algorithms can give a first estimation of disparities in epipolar (x-) direction, but the recursive algorithm can also start with zero-...

In this paper, we propose an efficient framework for edge-preserving stereo matching. Local methods for stereo matching are more suitable than global methods for real-time applications. Moreover, we can obtain accurate depth maps by using edge-preserving filter for the cost aggregation process in local stereo matching. The computational cost is high, since we must perform the filter for every n...

One approach to developing faster, more robust stereo algorithms is to seek a more complete and efficient use of information available in stereo images. The use of chromatic (color) information has been largely neglected in this regard. Motivations for using chromatic information are discussed, including strong evidence for the use of chromatic information in the human stereo correspondence pro...

General stereo image matching provides an adequate but hard problem with suucient complexity, with which the potential of wavelets may be exploited to a full extent. An ideal stereo image matching algorithm is supposed to be invariant to the scale, translation, rotation, and partial correspondence between two given stereo images. While the multiresolution of wavelets is good at scale adaptivity...

Stereo matching in non-ideal illumination is a challenging area of research. It assumes identical corresponding color values and this assumption is not guaranteed for real-time environment. As a result most of the stereo algorithms fail to generate good disparity. This paper proposes a Modified Census Correlation (MCC) data cost for stereo matching. The proposed data cost will be derived from m...