A spatial domain optimal trade-off Maximum Average Correlation Height (OT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the input image background clutter and normalised for local intensity changes. In this paper we compare the performance of the spatial domain (SPOT-MACH) filter to the widely applied data driven technique known as the Scale Invariant Feature Transform (SIFT). The SPOT-MACH filter is shown to provide more robust recognition performance than the SIFT technique for demanding images such as scenes in which there are large illumination gradients. The SIFT method depends on reliable local edge-based feature detection over large regions of the image plane which is compromised in some of the demanding images we examined for this work. The disadvantage of the SPOTMACH filter is its numerically intensive nature since it is template based and is implemented in the spatial domain.

Date Published: 20 April 2015
PDF: 15 pages
Proc. SPIE 9477, Optical Pattern Recognition XXVI, 947706 (20 April 2015); doi: 10.1117/12.2177451

Published in SPIE Proceedings Vol. 9477:
Optical Pattern Recognition XXVI
David Casasent; Mohammad S. Alam, Editor(s)