Abstract :
In this paper, our corner-based interest point detector, Robust Local Zernike Moment based Features (R-LZMF), which was proved to be scale, rotation and translation-invariant, is investigated for invariance against affine transformation, lighting and blurring. Furthermore, R-LZMF´s corner detection capability with Zernike moments of order 4 is theoretically explained in detail. Experimental results on the Inria Dataset show that R-LZMF outperforms SIFT, CenSurE, ORB, BRISK and competes with SURF in terms of repeatability for images under affine transformation and photometric deformation such as lighting and blurring.
Keywords :
affine transforms; edge detection; feature extraction; BRISK; CenSurE; ORB; R-LZMF corner detection capability; SIFT; affine transformation; blurring; corner-based interest point detector; image repeatability; lighting; local Zernike moments; photometric deformation; robust local Zernike moment-based features; rotation invariance; scale invariance; translation invariance; Computer vision; Conferences; Detectors; Feature extraction; Lighting; Robustness; corner detection; feature extraction; interest point detection; local Zernike moment;
