Bayesian matching technique for detecting simple objects in heavily noisy environment

John S. Baras; Emmanuel N. Frantzeskakis

doi:10.1117/12.48392

1 October 1991 Bayesian matching technique for detecting simple objects in heavily noisy environment

John S. Baras, Emmanuel N. Frantzeskakis

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48392
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

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John S. Baras and Emmanuel N. Frantzeskakis "Bayesian matching technique for detecting simple objects in heavily noisy environment", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48392

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