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Proceedings Paper

Introduction to the recognition of patterns in compressed data: optical processing of data transformed by block-, transform-, and runlength-encoding, as well as vector quantization
Author(s): Mark S. Schmalz
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Paper Abstract

We have recently shown that the processing of compressed and encrypted imagery can achieve computational speedup and data security by processing fewer data, which are encoded in an obscure format [5- 9]. Our previous work in compressive processing produced numerous image processing algorithms that yielded computational speedups on the order of the compression ratio. In Part 1 of this series [1], we discuss the theoretical basis for pattern recognition over compressed imagery. In this paper, we present theory in support of optical or electro-optical implementations of convolution or correlation operations over block-, transform-, and runlength-encoded imagery, as well as data encoded by vector quantization (VQ). Unlike our previous work in this area, we do not derive operations that return a compressed result. Instead, our algorithms produce a map of correlation coefficients in the image domain, using a compressed image as input. Several of our architectures could, in principle, perform in time that is at least proportional to the compression ratio. Theory is expressed in terms of image algebra, an emerging branch of mathematics that unifies linear and nonlinear mathematics in the image domain. Image algebra has been implemented on a variety of workstations and parallel processors, as well as electro-optical processors. Thus our algorithms are feasible as well as portable Analyses emphasize computational complexity and information loss.

Paper Details

Date Published: 28 March 1995
PDF: 16 pages
Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); doi: 10.1117/12.205780
Show Author Affiliations
Mark S. Schmalz, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 2490:
Optical Pattern Recognition VI
David P. Casasent; Tien-Hsin Chao, Editor(s)

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