Building of a recognition algorithms (RA) based on the selection of representative pseudo-objects and providing a solution to the problem of recognition of objects represented in a big-dimensionality feature space (BD...
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ISBN:
(纸本)9783030941413;9783030941406
Building of a recognition algorithms (RA) based on the selection of representative pseudo-objects and providing a solution to the problem of recognition of objects represented in a big-dimensionality feature space (BDFS) are described in this article. The proposed approach is based on the formation of a set of 2D basic pseudo-objects and the determination of a suitable set of 2D proximityfunctions (PF) when designing an extreme RA. The article contains a parametric description of the proposed RA. It is presented in the form of sequence of computational procedures. And the main ones are procedures for determining: the functions of differences among objects in a 2D subspace of representative features (TSRF);groups of interconnectedness pseudo-objects (GIPO) in the same subspace;a set of basic pseudo-objects;functions of differences between the basic pseudo-object in a TSRF. There are also groups of interconnectedness and basic PF;the integral recognizing operator with respect to basic PF. The results of a comparative analysis of the proposed and known RA are presented. The main conclusion is that the implementation of the approach proposed in this paper makes it possible to switch from the original BDFS to the space of representative features (RF), the dimension of which is significantly lower.
Recognizing objects within large-dimensional feature spaces presents significant challenges in constructing effective recognition algorithms. This study proposes a groundbreaking approach that harnesses two-dimensiona...
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Recognizing objects within large-dimensional feature spaces presents significant challenges in constructing effective recognition algorithms. This study proposes a groundbreaking approach that harnesses two-dimensional threshold functions to address the recognition problem with precision. The key innovation lies in the selection of representative pseudo-objects, which serve as the foundation for constructing two-dimensional threshold functions in the recognition algorithm model. Experimental studies were conducted, focusing specifically on face recognition, to assess the performance of these algorithms. The results of the above studies have shown that the proposed statistical algorithms increase the recognition accuracy and reduce the time of recognition of face parts and the face as a whole, described in the space of interrelated features. This study highlights the potential applications of these algorithms in diverse software systems tailored to solving practical recognition problems within the constraints of large-dimensional feature spaces.
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