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Proceedings Paper

Implementation of the Maximum Average Correlation Height (MACH) filter in the spatial domain for object recognition from clutter backgrounds
Author(s): Akber Gardezi; Philip Birch; Ioannis Kypraios; Rupert Young; Christopher Chatwin
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Paper Abstract

A moving space domain window is used to implement a Maximum Average Correlation Height (MACH) filter which can be locally modified depending upon its position in the input frame. This enables adaptation of the filter dependant on locally variant background clutter conditions and also enables the normalization of the filter energy levels at each step. Thus the spatial domain implementation of the MACH filter offers an advantage over its frequency domain implementation as shift invariance is not imposed upon it. The only drawback of the spatial domain implementation of the MACH filter is the amount of computational resource required for a fast implementation. Recently an optical correlator using a scanning holographic memory has been proposed by Birch et al [1] for the real-time implementation of space variant filters of this type. In this paper we describe the discrimination abilities against background clutter and tolerance to in-plane rotation, out of plane rotation and changes in scale of a MACH correlation filter implemented in the spatial domain.

Paper Details

Date Published: 13 May 2010
PDF: 9 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76961S (13 May 2010); doi: 10.1117/12.850216
Show Author Affiliations
Akber Gardezi, Univ. of Sussex (United Kingdom)
COMSATS Institute of Information Technology (Pakistan)
Philip Birch, Univ. of Sussex (United Kingdom)
Ioannis Kypraios, Univ. of Sussex (United Kingdom)
Rupert Young, Univ. of Sussex (United Kingdom)
Christopher Chatwin, Univ. of Sussex (United Kingdom)


Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; Abhijit Mahalanobis; David P. Casasent; Tien-Hsin Chao; Steven L. Chodos; William E. Thompson, Editor(s)

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