This paper presents an implementation of a modified parallel-pyramidal algorithm for efficient image processing and identification. The method involves the creation of a system model that supports the integration of spatial, temporal and network data to form a dynamic pyramidal-hierarchical network. The paper details vector sorting techniques, Gtransformations for modifying vector elements, and a shifting procedure that facilitates efficient data transformation. The procedures described are integrated into a general data processing sequence that involves iterative application of these methods until the final result is achieved. How the algorithm works is shown in the example of laser beam projection analysis.
This article presents the implementation of a modified parallel-pyramidal algorithm for the efficient processing and identification of images. The method involves creating a systemic model that supports spatial, temporal, and network data integration, forming a dynamic pyramidal-hierarchical network. The article details the techniques for sorting vectors, G-transformations for modifying vector elements, and the shifting procedure that facilitates efficient data transformation. The described procedures are integrated into the overall data processing sequence, which includes the iterative application of these methods until the final result is achieved.
The paper discusses the pyramid methods of direct and inverse Q-transformation. Examples of method implementation based on numerical data transformation are considered. The work is dedicated to the development of a method of pyramid transformation based on contour Q-transformation for encoding and processing images. The method of pyramid Q-transformation and an example of its application are detailed, and the results of its software model are analyzed.
This article examines the structures of one of the types of pyramidal networks - parallel-hierarchical networks. The presented material has a dual purpose - applied and cognitive. The first one is of great importance in increasing the "intelligence" of specialized computer tools using a bionic approach. The second one opens completely new possibilities for a deeper understanding of the structure of the brain from the perspective of the cybernetic approach.
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