Pattern Theory Extensions for Vector Quantization
Small Business Information
Frontier Technology, Inc.
4141 Col Glenn Hwy., Suite, 140, Beavercreek, OH, 45431
Mr. Terry Keller
AbstractPattern Theory is an engineering theory of algorithm design which provides a robust characterization of all types of patterns. Given a problem description in terms of a truth table and a special measure of pattern-ness, called Decomposed Function Cardinality (DFC), the theory conjectures that the desired solution among a set of candidates is the one that possesses the lowest DFC. The proposed Phase I research program will extend Pattern Theory algorithms to handle more input parameters and multi-level outputs for vector quantization applications. Vector quantization is a block encoding scheme used frequently in digitized speech and image data compression. This effort will examine the feasibility and utility of Pattern Theory generated vector quantization encoders/decoders. Conventional vector quantization performs a distance calculation to accomplish the encoding process, Pattern Theory could replace this with a much faster look-up table approach. Vector quantization look-up tables using Pattern Theory are developed from training vector examples. Training examples are reproduced perfectly but Pattern Theory generated look-up tables have a power of generalization for non-trained examples. This property will be explored for other applications including automatic target recognition and aircraft corrosion categorization under the optional "bridge" task.
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