Improving the performance of interest point detectors with contrast stretching functions
Prabakar Karuppannan Gunashekhar, Bahadir Kursat Gunturk · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
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.