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Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers (1994)
Pacific Grove, CA, USA
Oct. 31, 1994 to Nov. 2, 1994
ISSN: 1058-6393
ISBN: 0-8186-6405-3
pp: 741-748
P.C. Schaich , Lawrence Livermore Nat. Lab., CA, USA
G.A. Clark , Lawrence Livermore Nat. Lab., CA, USA
S.K. Sengupta , Lawrence Livermore Nat. Lab., CA, USA
K.-P. Ziock , Lawrence Livermore Nat. Lab., CA, USA
ABSTRACT
We report the development of an automatic image analysis system that detects gamma-ray source regions in images obtained from a coded aperture, gamma-ray imager. The number of gamma sources in the image is plot known prior to analysis. The system counts the number (K) of gamma sources detected in the image and estimates the lower bound for the probability that the number of sources in the image is K. The system consists of a two-stage pattern classification scheme in which the probabilistic neural network is used in the supervised learning mode. The algorithms were developed and tested using real gamma-ray images from controlled experiments in which the number and location of depleted uranium source disks in the scene are known.<>
INDEX TERMS
computer vision, signal detection, gamma-rays, image coding, learning (artificial intelligence), neural nets, image classification, probability
CITATION

P. Schaich, G. Clark, S. Sengupta and K. Ziock, "Computer vision for detecting and quantifying gamma-ray sources in coded-aperture images," Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers(ACSSC), Pacific Grove, CA, USA, 1995, pp. 741-748.
doi:10.1109/ACSSC.1994.471550
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