Methodological Principles of Pyramidal and Parallel-Hierarchical Image Processing on the Base of Neural-Like Network Systems

Abstract

Abstract —The scientific research deals with organization principles of parallel-hierarchical transform for multistage perception and processing, compression and recognition of information in informational structure and computational systems which make use of computational scheme similar to neural. Unified methodological approach was developed for analysis of parallel processes. This approach considers influence of structural hierarchy in dynamics, in other words it tracks processes of spatial areas transformation of correlated and generation of uncorrelated in time elements of generated network, at the time of transition of the network from one stable stage to another. Index Terms—parallel processing, parallel-hierarchical network, image processing, pattern recognition I. INTRODUCTION Constantly growing volume of data and computations, needed for processing of large arrays of information, for instance, images, requires higher level of performance of the systems involved in this process. Since the density of elements “package” in integrated circuits is determined by physical constraints, operation speed is limited by final speed of electromagnetic modes propagation from one element to another. It is possible to overcome this physical barrier only by means of pipelining of computational processes occurring in the system, that, in its turn, results in complication of system architecture. Intelligent processing of information requires consideration of each element in a certain context of its connections, and it is possible only in computing system, having topographic structure with 3D location of processing elements (PE). Such a structure of the system perfectly matches natural neurolike perception of sensor information [1, 2, 3]. The idea of hierarchical pyramidal processing of information was suggested for the first time in [4, 5, 6, 7]. Highly-efficient system of information processing could be realized adapting architecture to the corresponding data structure. But structure of data in the process of pyramidal processing changes from large fixed array on lower level to small flexible structure on upper level. The most interesting case is uniform non-distributed computational structures, which correspond to SIMD-syste m class, where some levels of identical PE operate in SIMD mode. Each level contains a great number of simple PE. Su ch system as PCLIP, PAPIA, GAM, SPHINX [8] belong to this group, as well as structures suggested by the authors and protected by certificates and patent for a device intended for realization of pyramidal processing of information [5]. In more sophisticated case of uniform distributed pyramidal computational structures some powerful identical processing units are integrated into hierarchical pyramidal structure. Each processing unit corresponds to a part of processed data. Such pyramidal system can op erate both in SIMD and in MIMD-modes. Computational structures [1, 5] suggested by the author and protected by th e certificates for a means and device, as well as systems Uhrs, Array/Net and EGPA [8] belong to this family. II. THE MAIN PRINCIPLES OF PYRAMIDAL AND PARALLEL-HIERARCHICAL PROCESSING OF DIGITAL INFORMATION Principle of construction of pyramidal hierarchical structure of data can be defined as a sequence of data arrays of the same information field at different levels of division: P = (A0, A1, A2, , AL), where A