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Illumination Invariant Data Cost using Modified Census Transform

Published on February 2014 by Raghavendra U, Krishnamoorthi Makkithaya, Karunakar A. K.
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National Conference on Recent Advances in Information Technology
Foundation of Computer Science USA
NCRAIT - Number 2
February 2014
Authors: Raghavendra U, Krishnamoorthi Makkithaya, Karunakar A. K.
96e8cc00-3759-4a90-80aa-5fa42562a699

Raghavendra U, Krishnamoorthi Makkithaya, Karunakar A. K. . Illumination Invariant Data Cost using Modified Census Transform. National Conference on Recent Advances in Information Technology. NCRAIT, 2 (February 2014), 38-41.

@article{
author = { Raghavendra U, Krishnamoorthi Makkithaya, Karunakar A. K. },
title = { Illumination Invariant Data Cost using Modified Census Transform },
journal = { National Conference on Recent Advances in Information Technology },
issue_date = { February 2014 },
volume = { NCRAIT },
number = { 2 },
month = { February },
year = { 2014 },
issn = 0975-8887,
pages = { 38-41 },
numpages = 4,
url = { /proceedings/ncrait/number2/15150-1417/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Advances in Information Technology
%A Raghavendra U
%A Krishnamoorthi Makkithaya
%A Karunakar A. K.
%T Illumination Invariant Data Cost using Modified Census Transform
%J National Conference on Recent Advances in Information Technology
%@ 0975-8887
%V NCRAIT
%N 2
%P 38-41
%D 2014
%I International Journal of Computer Applications
Abstract

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 modified census transformed indexed image and it is robust to change in lighting direction, exposure and illumination color. The obtained total energy is optimized for disparity estimation using Graph-Cut. An exhaustive evaluation using Middlebury stereo image proves the robustness of the proposed technique for variety of illumination and exposure conditions.

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Index Terms

Computer Science
Information Sciences

Keywords

Stereo Matching Radiometric Difference And Computer Vision