A Scale Invariant Interest Point Detector in Gabor Based Energy Space

Abstract Interest point detection is a fundamental issue in many intermediate level vision problems and plays a significant role in vision systems. The previous interest point detectors are designed to detect some special image structures such as corners, junctions, line terminations and so on. These detectors based on some simplified 2D feature models will not work for image features that differ significantly from the models. In this paper, a scale invariant interest point detector, which is appropriate for most types of image features, is proposed based on an iterative method in the Gabor based energy space. It detects interest points by noting that there are some similarities in the phase domain for all types of image features, which are obtained by different detectors respectively. Firstly, this approach obtains the positions of candidate points by detecting the local maxima of a series of energy maps constructed by Gabor filter responses. Secondly, an iterative algorithm is adopted to select the corresponding characteristic scales and accurately locate the interest points simultaneously in the Gabor based energy space. Finally, in order to improve the real-time performance of the approach, a fast implementation of Gabor function is used to accelerate the process of energy space construction. Experiments show that this approach has a broader applicability than the other detectors and has a good performance under rotation and some other image changes.