Application of Triple Features Theory to the Analysis of Half-Tone Images and Colored Textures. Feature Construction by Virtue of Stochastic Geometry and Functional Analysis

Nikolay Fedotov, Sergey Romanov, Daria Goldueva

Abstract


The existing methods of half-tone or color image recognition generally presuppose a prior simplification of the object to analyze. Such a simplification normally involves image binarization which may result in a loss of essential elements of information on the object. The paper proposes a new approach towards half-tone images and colored textures analysis and recognition by virtue of stochastic geometry and functional analysis. The method makes it possible to form both the recognition features to typify image geometric singularities, and the recognition features to reflect image brightness and color characteristics. According to the method suggested, recognition features can be generated in abundance - thousands of them - in an unattended mode, which provides for a most reliable image recognition. Moreover, the resulting features prove invariant both to a group of motions and to linear deformations, which is the key to the better part of image recognition problems.


Full Text: PDF DOI: 10.5539/cis.v6n4p17

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This work is licensed under a Creative Commons Attribution 3.0 License.

Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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