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Proceedings Paper

Improving the performance of interest point detectors with contrast stretching functions
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Paper Abstract

The initial stage of many computer vision algorithms such as object recognition and tracking is to detect interest points on an image. Some of the existing interest point detection algorithms are robust to illumination variations to a certain extent. We have recently proposed the contrast stretching technique to improve the repeatability rate of the Harris corner detector under large illumination changes5. In this paper the contrast stretching technique has been incorporated into two scale invariant interest point detectors, specifically multi-scale Harris and multi-scale Hessian detectors. We show that, with the adoption of contrast stretching technique, the performances of these detectors improve not only under illumination variations but also under variations of viewpoint, scale, blur, and compression. In addition, we discuss GPU implementation of the proposed technique.

Paper Details

Date Published: 6 March 2013
PDF: 11 pages
Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610B (6 March 2013); doi: 10.1117/12.2004789
Show Author Affiliations
Prabakar K. Gunashekhar, Lousiana State Univ. (United States)
Bahadir K. Gunturk, Louisiana State Univ. (United States)

Published in SPIE Proceedings Vol. 8661:
Image Processing: Machine Vision Applications VI
Philip R. Bingham; Edmund Y. Lam, Editor(s)

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