@conference {
	title = {Two-Dimensional Signal Deconvolution: Design Issues Related to a Novel Multisensor-based Approach},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1991},
	month = {07/1991},
	abstract = {We employ recent results of analysis in several complex variables to come up with a set of compactly supported approximate deconvolution kernels for the reconstruction of a two dimensional signal based on multiple linearly degraded versions of the signal with a family of kernels that satisfy suitable technical conditions. We discuss the question of convergence of the proposed deconvolution kernels, present simulation results that demonstrate the gain in bandwidth, and propose two data parallel grid layouts for the off-line computation of the deconvolution kernels.},
	address = {San Diego, California},
	booktitle = {SPIE 1991 International Symposium on Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision},
	pages = {pp. 356-366}
}
@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}
}
