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Proceedings Paper

Bayesian matching technique for detecting simple objects in heavily noisy environment
Author(s): John S. Baras; Emmanuel N. Frantzeskakis
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Paper Abstract

The template matching problem, for binary images corrupted with spatially white, binary, symmetric noise, is studied. Matching is compared based directly on the pixel-valued image data as well as on data coded by two simple schemes: a modification of the Hadamard basis and direct coarsening of resolution. Bayesian matching rules based on M-ary hypothesis tests are developed. The performance evaluation of these rules is provided. A study of the trade-off between the quantization level and the ability of detecting an object in the image is presented. This trade-off depends on the (external) noise generated at the moment the uncoded image is received. The sum-of-pixels and the histogram statistics are introduced in order to reduce the computational load inherent in the correlation statistic, with the resulting penalty of a higher probability of false alarm rate. The present work demonstrates by examples that it is beneficial for recognition to combine an image coding technique with the algorithm extracting some `basic' information from the image. In other words, coding (for compression) helps recognition. Numerical results illustrate this claim.

Paper Details

Date Published: 1 October 1991
PDF: 13 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48392
Show Author Affiliations
John S. Baras, Univ. of Maryland (United States)
Emmanuel N. Frantzeskakis, Univ. of Maryland (United States)

Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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