Evaluation of Interest Point Detectors for Scenes with Changing Lightening Conditions

The paper is aimed at the description of different image interest point detectors and their properties. Particularly, the Harris-Laplace detector, the Fast Hessian detector and the Difference of Gaussian detector are described. These algorithms have already been evaluated with respect to common geometrical transformations such as the rotation, the scale change, etc. This paper describes the testing process and the impact of brightness change and histogram equalization on the repeatability of tested detectors. The evaluation has been performed on two different image databases containing altogether four hundred and eighty nine images. The repeatability has been used for the evaluation of the described interest point detectors. The best results have been achieved for the Fast Hessian detector which has proved to be the fastest and also the most robust. The repeatability of the Fast Hessian detector has reached the value of 65.39% after performing the histogram equalization on the Caltech database.