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Geometric Encoding of Color Images

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Abstract

Formal analysis and computer recognition of 2D color images is an important branch of modern computer geometry. However, the present methods, in spite of their longstanding high development, are not quite satisfactory and seem to be much worse than (unknown) algorithms used by our brain to analyze visual information. Almost all existing algorithms omit colors and deal with gray scale transformations only. However, in many cases color information is important and has to be proceeded. In this paper a fundamentally new method of encoding and analyzing color digital images is proposed. The main idea of this method is that a full-color digital image is encoded by a special two-dimensional surface in the three-dimensional space. After that the surface 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 well-known algorithms).

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References

  1. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “SURF: Speeded Up Robust Features,” Comput. Vision and Image Understanding (CVIU) 110 (3), 346 (2008).

    Article  Google Scholar 

  2. J. Canny, “A Computational Approach for Edge Detection,” IEEE Trans. Pattern Anal, and Machine Intell. 8 (6), 679 (1986).

    Article  Google Scholar 

  3. D. G. Lowe, “Object Recognition from Local Scale-Invariant Features,” in Proc. Intern. Gonf. on Computer Vision, Kerkyra, Corfu, 1999 (IEEE Computer Society, 1999), pp. 1150–1157.

    Google Scholar 

  4. J. Beis and D. G. Lowe, “Shape Indexing Using Approximate Nearest-Neighbor Search in High-Dimensional Spaces,” in Conf. Computer Vision and Pattern Recognition, Puerto Rico, 1997 (IEEE Computer Society, 1997), pp. 1000–1006.

    Google Scholar 

  5. K. Mikolajczyk and C. Schmid, “A Performance Evaluation of Local Descriptors,” IEEE Trans. Pattern Anal, and Machine Intell. 10 (27), 1615 (2005).

    Article  Google Scholar 

  6. H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: Speeded Up Robust Features,” in Proc. Nineth Europ. Conf. on Computer Vision. Graz, Austria, May 2006 (Springer Verlag, Berlin Heidelberg, 2006).

    Google Scholar 

  7. T. Lindberg, “Image Matching Using Generalized Scale-Space Interest Points,” J. Math. Imaging and Vision 52 (1), 3 (2015).

    Article  MathSciNet  Google Scholar 

  8. R.-O. Ives and M. Delbracio, “Anatomy of the SIFT Method,” Image Processing On Line 4, 370 (2014).

    Article  Google Scholar 

  9. G. V. Nosovskii, “Computer Gluing of 2D Projective Images,” in Proc. Workshop "Contemporary Geometry and Related Topics", Belgrade, Yugoslavia, 15-21 May 2002 (World Scientific, New Jersey; London; Singapore, 2004) pp. 319–334.

    Chapter  Google Scholar 

  10. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge Univ. Press, Cambridge, 2000).

    MATH  Google Scholar 

  11. M. Hosaka, Modeling of Curves and Surfaces in CAD/GAM (Springer-Verlag, Berlin, Heidelberg, 1992).

    Book  Google Scholar 

  12. N. N. Golovanov, D. P. Il'yutko, G. V. Nosovskii, and A. T. Fomenko, Computer Geometry (Akadaemiya, Moscow, 2006) [in Russian].

    Google Scholar 

  13. G. V. Nosovskii and E. S. Skripka, “Error Estimation for the Direct Algorithm of Projective Mapping Calculation in Multiple View Geometry,” in Proc. Workshop "Contemporary Geometry and Related Topics". Belgrade, Serbia and Montenegro, June 26 July 2, 2005 (University of Belgrade, Faculty of Mathematics, Belgrade, 2006), pp. 399–408.

    Google Scholar 

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Correspondence to G. V. Nosovskii.

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Original Russian Text © G.V. Nosovskii. 2018. published in Vestnik Moskovskogo Universiteta. Matematika. Mekhanika. 2018. Vol. 73, No. 1, pp. 3-11.

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Nosovskii, G.V. Geometric Encoding of Color Images. Moscow Univ. Math. Bull. 73, 1–8 (2018). https://doi.org/10.3103/S0027132218010011

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  • DOI: https://doi.org/10.3103/S0027132218010011

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