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ISSN Druckformat: 0040-2508
ISSN Online: 1943-6009
Indexed in
EFFICIENT ESTIMATION OF VISUAL OBJECT RELEVANCE DURING RECOGNITION THROUGH THEIR VECTOR DESCRIPTIONS
ABSTRAKT
The paper is devoted to some aspects of structural descriptions matching in course of visual object recognition in computer vision systems. The suggested here structural recognition technique employs both the "tf-idf" method used in the information retrieval and the median processing of descriptions. This technique was successfully applied to transform a description consisting of a set of image features into a vector of features or a list of descriptors, which essentially reduces the computational costs. The experimental results confirmed the adequacy and efficiency of the suggested techniques.
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Gorokhovatskyi Oleksii, Gorokhovatskyi Volodymyr, Peredrii Olena, Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features, Data, 3, 4, 2018. Crossref
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Gorokhovatskyi Volodymyr, Gorokhovatskyi Oleksii, Yevgenyi Putyatin, Olena Peredrii, Quantization of the Space of Structural Image Features as a Way to Increase Recognition Performance, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 2018. Crossref
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Volodymyr Gorokhovatskyi, Svitlana Gadetska, Classification of Images of Visual Objects Based on Statistical Relevance Measures of Their Structural Descriptions, 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), 2018. Crossref