@conference {
	title = {A Bayesian Matching Technique for Detecting Simple Objects in Heavily Noisy Environment},
	author = {Baras, John S., Frantzeskakis, Emmanuel.},
	year = {1991},
	month = {07/1991},
	abstract = {The template matching problem for binary corrupted images with spatially white, binary, symmetric noise is studies. We compare matching based directly on the pixel-value image data as well as 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 are developed. The performance evaluation of these rules is provided,

The paper presents a study of the trade-off between the quantization level and the ability of detecting an object in an image. This trade-off depends on the (external) noise generated at the moment we receive the uncoded image.

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.

In the present work~we demonstrate by examples that it is beneficial for recognition to combine an image coding technique with an algorithm extracting some "basic" information from the image. In other words~coding (for compression) helps recognition. Numerical results illustrate this claim.},
	address = {San Diego, California},
	booktitle = {SPIE 1991 International Symposium on Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision},
	pages = {pp. 341-353}
}
