Image processing technique based on image understanding architecture

Igor Kuvychko

doi:10.1117/12.411582

28 December 2000 Image processing technique based on image understanding architecture

Igor Kuvychko

Proceedings Volume 4115, Applications of Digital Image Processing XXIII; (2000) https://doi.org/10.1117/12.411582
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

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.

Citation Download Citation

Igor Kuvychko "Image processing technique based on image understanding architecture", Proc. SPIE 4115, Applications of Digital Image Processing XXIII, (28 December 2000); https://doi.org/10.1117/12.411582

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