Spectrum shape elements model for correction of multichannel images
A.V. Nikonorov

PDF, 332 kB

Full text of article: Russian language.

DOI: 10.18287/0134-2452-2014-38-2-304-313

Pages: 304-313.

Abstract:
This paper presents the spectral-shape elements model for correction of non-isoplanatic deviation in the scene illumination. I propose an identification of the correction function on the set of spectral shape elements with the Hausdorff metric. Also a necessary condition which allows to obtain an adequate form of the color correction function is presented. The experiments performed on real images confirm the high quality of the proposed color correction technique with respect to well-known Retinex method.

Key words:
image processing, color correction, dichromatic model, spectral shape elements, Hausdorff distance, non-negative LSM, Retinex model, color constancy.

References:

  1. Zhuravlev, Yu.I. An Algebraic Approach to Recognition and Classification Problems // Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications. – 1998. – V. 8. – P. 59-100.
  2. Pytyev, Yu.P. Morphological methods of image analysis / Yu.P. Pytyev, A.I. Chulichkov. – Moscow: "Phizmatlit" Publisher, 2010. – 335 p. – (In Russian).
  3. Computer Image Processing, Part II: Methods and algorithms / M.V. Gashnicov, N.I. Glumov, N.Yu. Ilyasova, V.V. Myas–ni–cov, S.B. Popov, V.V. Sergeev, V.A. Soifer, A.G. Khramov, A.V. Chernov, V.M. Chernov, M.a. Chicheva, V.A. Fursov; ed.by V.A. Soifer – VDM Verlag, 2009. – 584 p.
  4. Rudakov, K.V. On algebraic theory of global and local constraints in classification problems // Recognition, classification, forecast. – 1989. – P. 176-201. – (In Russian).
  5. Gurevich, I.B. Descriptive Approach to Image Analysis: Image Models / I.B. Gurevich, V.V. Yashina // Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications. – 2008. – V. 18(4). – P. 518-541.
  6. Ritter, G.X. Handbook of Computer Vision Algorithms in Image Algebra / G.X. Ritter, J.N. Wilson. – CRC Press Inc., 2001.
  7. Michaelsen, E. Extraction of building polygons from SAR images: Grouping and decision-level in the GESTALT system / E. Michaelsen, U. Stilla, U. Soergel, L.J. Doktorski // Pattern Recognition Letters. – 2010. – V. 31(10). – P. 1071-1076.
  8. Sternberg, S.R. An overview of Image Algebra and Related Architectures, Integrated Technology for parallel Image Processing. – London: Academic Press, 1985.
  9. Serra, J. Image Analysis and Mathematical Morphology. – USA, Academic Press, Inc., 1983.
  10. Fergus, R. Removing Camera Shake From A Single Photograph / R. Fergus, B. Singh, A. Hertzmann, S.T. Roweis, W.T. Freeman // ACM Transactions on Graphics, SIGGRAPH’06. – 2006. – V. 25(3). – P. 787-794.
  11. Geraud, T. Color image segmentation based on automatic morphological clustering / T. Geraud, P.-Y. Strub, J. Darbon // IEEE Proc., Inter. Conf. on Image Processing, Thessaloniki, Greece. – 2001. – V. 3. – P. 70-73.
  12. Nieuwenhuis, C. Spatially Varying Color Distributions for Interactive Multi-Label Segmentation / C. Nieuwenhuis, D. Cremers // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2013. – V. 35(5). – P. 1234-1247.
  13. Land, E. The retinex theory of color vision // Scientific American. – V. 237(6). – P. 108-128.
  14. Brainard, D.H. Analysis of the retinex theory of color vision / D.H. Brainard, B.A. Wandell // JOSA. – 1986. – V. 3(10). – P. 1651-1661.
  15. Margulis, D. Modern Photoshop Color Workflow - The Quartertone Quandary, the PPW, and Other Ideas for Speedy Image Enhancement. – MCW Publishing, 2013. – 480 p.
  16. Moreno, A. Color Correction: A Novel Weighted Von Kries Model Based on Memory Colors / A. Moreno, B. Fernando, B. Kani, S. Saha, S. Karaoglu // CCIW'11 Third international conference on Computational color imaging. – 2011. – P. 165-175.
  17. Gijsenij, A. Computational Color Constancy: Survey and Experiments / A. Gijsenij, T. Gevers, J. van de Weijer // IEEE Transactions on Image Processing. – 2011. – V. 20(9). P. 2475-2478.
  18. Bavrina, A.Yu. Modelling of videoinformational tract of optoelectronic remote sensing systems of earth: solutions, problems and tasks / A.Yu. Bavrina, V.V. Myasnikov, V.V. Sergeev, E.V. Tresheva, N.V. Chupushev // Computer Optics. – 2012. – V. 36(4). – P. 572-585. – (In Russian).
  19. Sergeev, V.V. Parametric identification of spatial based distortions / V.V. Sergeev, V.V. Fursov, M.V. Maksimov // III International Conference “Pattern Recognition and Image Analysis” (PRIA-97), Nizhniy Novgorod, 1997. – V. 1. – P. 252-255. – (In Russian).
  20. Nikonorov, A. Illuminant color correction, using color shape units method // 11th International Conference on Pattern Recognition and Image Analysis: New Information Technologies. Samara, 2013. – V. I. – P. 276-279.
  21. Bibikov, S. Recognition of Artifacts in Digital Images Using Conjugacy Indicator / S. Bibikov, V. Fursov, A. Nikonorov, P. Yakimov // 8th Open German-Russian Workshop “Pattern Recognition and Image Understanding”. – Nizhny Novgorod, 2011. – P. 21-24.
  22. Fursov, V.A. Adaptive Identification by Small Number of Observations // “Information Technologies” Journal Add-ons. – 2013. – V. 9. – 32 p. – (In Russian).
  23. Judd, D.B. Color in Business, Science, and Industry / D.B. Judd, G. Wyszecki. – Willey, 1975. – 576 p.
  24. Cheng, H.D. Color Image Segmentation: Advances & Prospects / H.D. Cheng, X.H. Jiangm, Y. Sun, J.L. Wang // Pattern Recognition. – 2001. – V. 34(12). – P. 2259-2281.
  25. Chakrabarti, A. Computational Color Constancy with Spatial Correlations / A. Chakrabarti, K.  Hirakawa, T  Zickler // TR-09-10. – Cambridge: Harvard University, 2013. – 13 p.
  26. Salvador, E. Shadow segmentation and tracking in real-world conditions // PhD Thesis. – EPFL. – 2004. – 194 p.
  27. Klinker, G.J. The measurement of highlights in color images / G.J. Klinker, S.A. Shafer, T. Kannade // International Journal of Computer Vision. – 1988. – V. 2. – P. 7-32.
  28. Pan, Z. Properties of Self-Replicating Cellular Automata Systems Discovered Using Genetic Programming / Z. Pan, J. Reggia, D. Gao // Advances in Complex Systems. – 2007. – V. 10(1). – P. 61-84.
  29. Rosin, P.L. Training Cellular Automata for Image Processing / P.L. Rosin // IEEE Transactions on Image Processing. – 2006. – V. 15(7). – P. 2076-2087.
  30. Volotovsky, S. Computer vision system for recognition of railway tanks numbers using a modified correlator in the Hausdorff metric / S.G. Volotovsky, N.L. Kazanskiy, S.B. Popov, R.V. Hmelev // Computer Optics. – 2005. – V. 27. – P. 177-185. – (In Russian).
  31.  Llanas, B. Efficient Computation of the Hausdorff Distance Between Polytopes by Exterior Random Covering // Computational Optimization and Applications. – 2005. – V. 30 – P. 161-194.
  32. Lawsen, Ch.L. Solving Least Squares Problems. New Jersey: Prentice-Hall, Inc., Englenood Cliffs, 1974. – 320 p.
  33. Limare,  N. Retinex Poisson Equation: a Model for Color Perception / N. Limare, A.B. Petro, C. Sbert, J.M. Morel // Image Processing On Line. – 2011.
  34. Torr, P. The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix / P. Torr, D. Murray // International Journal of Computer Vision. – 1997. – V. 24(3). – P. 271-300.
© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20