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7 March 2014 Illumination-invariant pattern recognition using fringe-adjusted joint transform correlator and monogenic signal
Paheding Sidike, Vijayan K. Asari, Mohammad S. Alam
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Proceedings Volume 9024, Image Processing: Machine Vision Applications VII; 90240C (2014) https://doi.org/10.1117/12.2040752
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
Abstract
The joint transform correlator (JTC) technique has shown attractive performance for real-time pattern recognition applications. Among the various JTC techniques proposed in the literature, the fringe-adjusted JTC (FJTC) yields remarkable promise for object recognition, and it has been shown that the FJTC produces a better correlation output than alternate JTCs under varying illumination conditions of the input scene; however, it has been found that the FJTC is not illumination invariant. Therefore, to alleviate this drawback of the FJTC, an illumination-invariant FJTC, based on combination of the fringe-adjusted filter (FAF) and the monogenic signal, is presented. The performance of the FJTC and the proposed local phase based FJTC technique in unknown input-image with varying illumination is investigated and compared. The proposed detection algorithm makes use of the monogenic signal from a two dimensional object region to extract the local phase information for assisting the FJTC robust to illumination effects. Experimental results show that by utilizing the monogenic phase information enables the FAF-based JTC to produce sharper correlation peaks and higher peak-to-clutter ratio compared to alternate JTCs. The proposed technique may be used as a real-time region-ofinterest identifier in wide-area surveillance for automatic object recognition when the target under very dark or bright condition that beyond human vision.
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Paheding Sidike, Vijayan K. Asari, and Mohammad S. Alam "Illumination-invariant pattern recognition using fringe-adjusted joint transform correlator and monogenic signal", Proc. SPIE 9024, Image Processing: Machine Vision Applications VII, 90240C (7 March 2014); https://doi.org/10.1117/12.2040752
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KEYWORDS
Pattern recognition
Target detection
Joint transforms
Optical correlators
Detection and tracking algorithms
Electronic filtering
Object recognition
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