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@inproceedings{785293,
author = {Williams, Ian and Svoboda, David and Bowring, Nicholas and Guest, Elizabeth},
address = {Bellingham, Washington},
booktitle = {Proceedings of SPIE-IS&T Electronic Imaging},
edition = {Vol. 6812},
keywords = {statistical edge detection; neural networks; image processing},
language = {eng},
location = {Bellingham, Washington},
isbn = {978-0-8194-6984-7},
pages = {68121J-1-12},
publisher = {SPIE},
title = {Statistical Edge Detection of Concealed Weapons Using Artificial Neural Networks},
url = {http://dx.doi.org/10.1117/12.766472},
year = {2008}
}
TY - JOUR
ID - 785293
AU - Williams, Ian - Svoboda, David - Bowring, Nicholas - Guest, Elizabeth
PY - 2008
TI - Statistical Edge Detection of Concealed Weapons Using Artificial Neural Networks
PB - SPIE
CY - Bellingham, Washington
SN - 9780819469847
KW - statistical edge detection
KW - neural networks
KW - image processing
UR - http://dx.doi.org/10.1117/12.766472
N2 - A novel edge detector has been developed that utilizes 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 analyzing 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. The range and type of tests has been greatly expanded from the previous works. 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.
ER -
WILLIAMS, Ian, David SVOBODA, Nicholas BOWRING and Elizabeth GUEST. Statistical Edge Detection of Concealed Weapons Using Artificial Neural Networks. In \textit{Proceedings of SPIE-IS\&{}T Electronic Imaging}. Vol. 6812. Bellingham, Washington: SPIE, 2008, p.~68121J-1-12, 12 pp. ISBN~978-0-8194-6984-7.
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