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Proceedings Paper

Image processing technique based on image understanding architecture
Author(s): Igor Kuvychko
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Paper Abstract

Effectiveness of image applications is directly based on its abilities to resolve ambiguity and uncertainty in the real images. That requires tight integration of low-level image processing with high-level knowledge-based reasoning, which is the solution of the image understanding problem. This article presents a generic computational framework necessary for the solution of image understanding problem -- Spatial Turing Machine. Instead of tape of symbols, it works with hierarchical networks dually represented as discrete and continuous structures. Dual representation provides natural transformation of the continuous image information into the discrete structures, making it available for analysis. Such structures are data and algorithms at the same time and able to perform graph and diagrammatic operations being the basis of intelligence. They can create derivative structures that play role of context, or 'measurement device,' giving the ability to analyze, and run top-bottom algorithms. Symbols naturally emerge there, and symbolic operations work in combination with new simplified methods of computational intelligence. That makes images and scenes self-describing, and provides flexible ways of resolving uncertainty. Classification of images truly invariant to any transformation could be done via matching their derivative structures. New proposed architecture does not require supercomputers, opening ways to the new image technologies.

Paper Details

Date Published: 28 December 2000
PDF: 11 pages
Proc. SPIE 4115, Applications of Digital Image Processing XXIII, (28 December 2000); doi: 10.1117/12.411582
Show Author Affiliations
Igor Kuvychko, Association for Computing Machinery (United States)

Published in SPIE Proceedings Vol. 4115:
Applications of Digital Image Processing XXIII
Andrew G. Tescher, Editor(s)

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