Abstract:
Formal analysis and computer recognition of 2D color images is an important branch of modern computer geometry. However, existing algorithms, although they are highly developed, are not quite satisfactory and seem to be much worse than (unknown) algorithms, which our brain uses to analyze eye information. Almost all existing algorithms omit colors and deal with grayscale transformations only. But in many cases color information is important. In this paper a fundamentally new method of coding and analyzing color digital images is proposed. The main point of this method is that a full-color digital image is represented without dropping colors by a special 2D surface in 3D space, after which it is analyzed by methods of differential geometry rather than traditional gradient-based or Hessian-based methods (like SIFT, GLOH, SURF, Canny operator, and many other algorithms).
This publication is cited in the following 2 articles:
G. V. Nosovskii, A. Yu. Chekunov, “Implementation of a fast post-processing algorithm of geometrical coding of digital images using CUDA architecture”, Moscow University Mathematics Bulletin, 77:6 (2022), 296–301
A. Yu. Chekunov, “Implementation of the fast algorithm for geometrical coding of digital images with the use of CUDA architecture”, Moscow University Mathematics Bulletin, 73:6 (2018), 229–238