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Proceedings Paper

Performance assessment of Unconstrained Hybrid Optical Neural Network (U-HONN) filter for object recognition tasks in clutter
Author(s): Ioannis I. Kypraios; Rupert C.D. Young; Chris R. Chatwin
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Paper Abstract

Previously we have described a hybrid optical neural network (HONN) filter. The filter is synthesised employing an artificial neural network technique that generates a non-linear interpolation of the intermediate train set poses of the training-set objects but maintains linear shift-invariance which allows potential implementation within a linear optical correlator type architecture. In this paper, we remove the constraints imposed on the filter’s output correlation peak height from the constraint matrix of the synthetic discriminant function used to create the composite filter. We examine the U-HONN filter’s detectability, peak sharpness, within-class distortion range, discrimination ability between an in-class and out-of-class object and the filter’s tolerance to clutter. We assess the behaviour of the U-HONN filter in an open area surveillance application. The filter demonstrates good object detection abilities within cluttered scenes, keeping good quality correlation peak sharpness and detectability throughout all the sets of tests. Thus the U-HONN filter is able to detect and accurately classify the in-class object within different background scenes at intermediate angles to the train-set poses.

Paper Details

Date Published: 12 April 2004
PDF: 12 pages
Proc. SPIE 5437, Optical Pattern Recognition XV, (12 April 2004); doi: 10.1117/12.542058
Show Author Affiliations
Ioannis I. Kypraios, Univ. of Sussex (United Kingdom)
Rupert C.D. Young, Univ. of Sussex (United Kingdom)
Chris R. Chatwin, Univ. of Sussex (United Kingdom)


Published in SPIE Proceedings Vol. 5437:
Optical Pattern Recognition XV
David P. Casasent; Tien-Hsin Chao, Editor(s)

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