INVESTIGATION OF THE HIERARCHICAL FEATURES SYSTEM IN THE BLOCK FEED OF THE DESCRIPTION IN THE COMPOSITION OF THE IMAGE KEY POINT
DOI:
https://doi.org/10.26906/SUNZ.2019.2.069Keywords:
structural image recognition methods, key point, ORB detector, descriptor, fragment data distribution, descriptive relevance, Manhattan metric, speed of relevancy estimationAbstract
The subjects of the paper are the hierarchical models for estimation of the image descriptions relevance when recognizing visual objects in computer vision systems. The goal is to modify an image structural recognition method based on the implementation of block data models with the integration of probability distributions. The tasks are: include the development of mathematical and software models of efficient hierarchical data processing for determining the relevance of structural descriptions, investigation of the properties of these models, evaluation of the effectiveness of image processing. The methods are used: an ORB detector to form the key point descriptors, data mining, methods for construction of the bit-data distribution, a method of metric relevance estimation, software modeling. The following results were obtained. The transition from the sets of descriptors to distributions of fragments, the construction of hierarchical features provide the necessary recognition performance. Data processing and analysis are performed several times faster compared to ones based on distributions. Conclusions. The contribution of the paper is the improvement of the structural image recognition method with the introduction of a block description structure using integrated distribution values for fragments of the set of key point descriptors. The practical significance of the paper is the achievement of an increase of image relevance calculation speed, confirmation of the effectiveness of hierarchical features using image examples, obtaining of an application software models for research and implementation of classification methods in computer vision systemsDownloads
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