6 March 2013 Improving the performance of interest point detectors with contrast stretching functions
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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.
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Prabakar K. Gunashekhar, Bahadir K. Gunturk, "Improving the performance of interest point detectors with contrast stretching functions", Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610B (6 March 2013); doi: 10.1117/12.2004789; https://doi.org/10.1117/12.2004789
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