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Proceedings Paper

Statistical edge detection of concealed weapons using artificial neural networks
Author(s): Ian Williams; David Svoboda; Nicholas Bowring; Elizabeth Guest
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Paper Abstract

A novel edge detector has been developed that utilises statistical masks and neural networks for the optimal detection of edges over a wide range of image types. The failure of many common edge detection techniques has been observed when analysing concealed weapons X-ray images, biomedical images or images with significant levels of noise, clutter or texture. This novel technique is based on a statistical edge detection filter that uses a range of two-sample statistical tests to evaluate any local image texture differences and by applying a pixel region mask (or kernel) to the image analyse the statistical properties of that region. The range and type of tests has been greatly expanded from the previous work of Bowring et al.1 This process is further enhanced by applying combined multiple scale pixel masks and multiple statistical tests, to Artificial Neural Networks (ANN) trained to classify different edge types. Through the use of Artificial Neural Networks (ANN) we can combine the output results of several statistical mask scales into one detector. Furthermore we can allow the combination of several two sample statistical tests of varying properties (for example; mean based, variance based and distribution based). This combination of both scales and tests allows the optimal response from a variety of statistical masks. From this we can produce the optimum edge detection output for a wide variety of images, and the results of this are presented.

Paper Details

Date Published: 3 March 2008
PDF: 12 pages
Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 68121J (3 March 2008); doi: 10.1117/12.766472
Show Author Affiliations
Ian Williams, Manchester Metropolitan Univ. (United Kingdom)
David Svoboda, Masaryk Univ. (Czech Republic)
Nicholas Bowring, Manchester Metropolitan Univ. (United Kingdom)
Elizabeth Guest, Leeds Metropolitan Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 6812:
Image Processing: Algorithms and Systems VI
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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