Invariant Gabor-based interest points detector under geometric transformation

This paper proposes a novel approach for detecting interest points invariant to rotation and scale using Gabor wavelet. Our scale and rotational invariant interest points detector is produced based on a multi-scale representation by selecting points at different scales from a combination of multi-orientations response maps using a non-maximum suppressing technique. We present a comparative evaluation of our proposed detector with the existing detectors. Experimental results show that our proposed method outperforms other methods under different viewpoint angles and provides a comparable results to the existing methods under scale changes for a set of test images with different geometric transformations.