Title :
Computer vision for detecting and quantifying gamma-ray sources in coded-aperture images
Author :
Schaich, Paul C. ; Clark, Gregory A. ; Sengupta, Sailes K. ; Ziock, Klaus-Peter
Author_Institution :
Lawrence Livermore Nat. Lab., CA, USA
fDate :
31 Oct-2 Nov 1994
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
Keywords :
computer vision; gamma-rays; image classification; image coding; learning (artificial intelligence); neural nets; probability; signal detection; U; algorithms; automatic image analysis system; coded-aperture images; computer vision; controlled experiments; depleted uranium source disks; gamma-ray source regions; gamma-ray sources detection; lower bound; probabilistic neural network; probability; supervised learning mode; two-stage pattern classification; Apertures; Computer vision; Gamma ray detection; Gamma ray detectors; Image analysis; Layout; Neural networks; Pattern classification; Supervised learning; Testing;
Conference_Titel :
Signals, Systems and Computers, 1994. 1994 Conference Record of the Twenty-Eighth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-6405-3
DOI :
10.1109/ACSSC.1994.471550
