Authors:
Gökhan Özbulak
and
Muhittin Gökmen
Affiliation:
Istanbul Technical University, Turkey
Keyword(s):
Interest Point Detection, Feature Extraction, Object Detection, Local Zernike Moments, Scale-Space.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Registration
;
Shape Representation and Matching
Abstract:
In this paper, a novel interest point detector based on Local Zernike Moments is presented. Proposed
detector, which is named as Robust Local Zernike Moment based Features (R-LZMF), is invariant to scale,
rotation and translation changes in images and this makes it robust when detecting interesting points across
the images that are taken from same scene under varying view conditions such as zoom in/out or rotation.
As our experiments on the Inria Dataset indicate, R-LZMF outperforms widely used detectors such as SIFT
and SURF in terms of repeatability that is main criterion for evaluating detector performance.