Abstract
The relevance of the results of search, classification, and description of the shapes of objects in images largely depends on the quality of vectorization, i.e., on determining regions that are uniform in color and texture and on constructing their boundaries. A color image segmentation algorithm that clusterizes the color palette of the image by constructing the three-dimensional histogram in the color space HSV is proposed. A feature of this algorithm is that it searches the local maximums on the histogram by scanning the color space with a three-dimensional neighborhood analysis operator. Furthermore, an algorithm for approximating boundaries of regions by line segments and circular arcs that recursively augments the approximated chains is proposed. These algorithms are designed for automating the process of extracting informative features from images for the purpose of using them in computer vision systems, content-based image retrieval systems, geographic information systems and other decision support systems based on graphical information.
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Translated by A. Klimontovich
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Kasimov, D.R., Kuchuganov, A.V., Kuchuganov, V.N. et al. Approximation of Color Images Based on the Clusterization of the Color Palette and Smoothing Boundaries by Splines and Arcs. Program Comput Soft 44, 295–302 (2018). https://doi.org/10.1134/S0361768818050043
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DOI: https://doi.org/10.1134/S0361768818050043