G-YZMÖ: Gürbüz yerel zernike moment tabanlı özellikler

In this thesis, a novel rotation and translation invariant local Zernike moment based interest point detection algorithm is presented and named as Local Zernike Moment based Features (LZMF). LZMF is then extended to have scale-invariant characteristic by constructing image pyramid in scale-space. Final detector is scale, rotation and translation invariant, and also robust to background clutter and occlusion. This final detector is named as Robust Local Zernike Moment based Features or R-LZMF shortly. R-LZMF is a corner based interest point detector and uses local Zernike moments as convolutional operators in order to detect corners in spatial-space. In this way, descriptive power of Zernike moments is utilized in local sense by applying them to the image pixels and thus structure of corners can be successfully exposed. Performance of proposed interest point detection algorithms, LZMF and R-LZMF, are evaluated on the Inria Dataset by using repeatability score, which is the main criterion for detector accuracy, and the performance of proposed algorithms is compared to well known interest point detectors such as Harris, SIFT, SURF, CenSurE, BRISK for LZMF and SIFT, SURF, CenSurE, ORB, BRISK for R-LZMF. Evaluation results on "Rotation", "Zoom" and "Zoom&Rotation" sequences of the Inria Dataset show that LZMF and R-LZMF outperform almost all interest point detectors to be compared in terms of repeatability score. Distinctiveness performance of LZMF and R-LZMF are also presented by applying the detectors on to the synthetic and real images that contain corner points.