Analysis and coding technique based on computational intelligence methods and image-understanding architecture

Abstract

Human vision involves higher-level knowledge and top-bottom processes for resolving ambiguity and uncertainty in the real images. Even very advanced low-level image processing can not give any advantages without a highly effective knowledge-representation and reasoning system that is the solution of image understanding problem. Methods of image analysis and coding are directly based on the methods of knowledge representation and processing. Article suggests such models and mechanisms in form of Spatial Turing Machine that in place of symbols and tapes works with hierarchical networks represented dually as discrete and continuous structures. Such networks are able to perform both graph and diagrammatic operations being the basis of intelligence. Computational intelligence methods provide transformation of continuous image information into the discrete structures, making it available for analysis. Article shows that symbols naturally emerge in such networks, giving opportunity to use symbolic operations. Such framework naturally combines methods of machine learning, classification and analogy with induction, deduction and other methods of higher level reasoning. Based on these principles image understanding system provides more flexible ways of handling with ambiguity and uncertainty in the real images and does not require supercomputers. That opens way to new technologies in the computer vision and image databases.