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Proceedings Paper

A robust fringe-adjusted joint transform correlator for efficient object detection
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Paper Abstract

The fringe-adjusted joint transform correlation (FJTC) technique has been widely used for real-time optical pattern recognition applications. However, the classical FJTC technique suffers from target distortions due to noise, scale, rotation and illumination variations of the targets in input scenes. Several improvements of the FJTC have been proposed in the literature to accommodate these problems. Some popular techniques such as synthetic discriminant function (SDF) based FJTC was designed to alleviate the problems of scale and rotation variations of the target, whereas wavelet based FJTC has been found to yield better performance for noisy targets in the input scenes. While these techniques integrated with specific features to improve performance of the FJTC, a unified and synergistic approach to equip the FJTC with robust features is yet to be done. Thus, in this paper, a robust FJTC technique based on sequential filtering approach is proposed. The proposed method is developed in such a way that it is insensitive to rotation, scale, noise and illumination variations of the targets. Specifically, local phase (LP) features from monogenic signal is utilized to reduce the effect of background illumination thereby achieving illumination invariance. The SDF is implemented to achieve rotation and scale invariance, whereas the logarithmic fringe-adjusted filter (LFAF) is employed to reduce the noise effect. The proposed technique can be used as a real-time region-of-interest detector in wide-area surveillance for automatic object detection. The feasibility of the proposed technique has been tested on aerial imagery and has observed promising performance in detection accuracy.

Paper Details

Date Published: 20 April 2015
PDF: 8 pages
Proc. SPIE 9477, Optical Pattern Recognition XXVI, 947707 (20 April 2015); doi: 10.1117/12.2086848
Show Author Affiliations
Paheding Sidike, Univ. of Dayton (United States)
Vijayan K. Asari, Univ. of Dayton (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)


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

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