A rotation invariant local Zernike moment based interest point detector

Abstract

Detection of interesting points in the image is an important phase when considering object detection problem in computer vision. Corners are good candidates as such interest points. In this study, by optimizing corner model of Ghosal based on local Zernike moments (LZM) and using LZM representation Sariyanidi et.al presented, a rotation-invariant interest point detector is proposed. The performance of proposed detector is evaluated by using Mikolajczyk's dataset prepared for rotation-invariance and our method outperforms well-known methods such as SIFT and SURF in terms of repeatability criterion.