Abstract—
It is described how to construct a distance on analog and quantized images, which is invariant with respect to strictly monotonic increasing transformations of the brightness function. The distance function takes into account possible normal noise on the image. The maximal value of such a distance for any number of quantizing levels and noise parameters is calculated. The experimental results, which make it possible to compare the invariant distance measure with classical measures, are presented.
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REFERENCES
W. C. Wilder, “Subjective Relevant Error Criteria for Pictorial Data Processing,” Report TR-EE 72-34 (Purdue University, School of Electrical Engineering, West Lafayette, 1972).
Yu. I. Monich and V. V. Starovoitov, “Image quality evaluation for digital image analysis,” Iskusstv. Intellekt (Artif. Intell.), No. 4, 376–386 (2008) [in Russian].
T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recogn. 29 (1), 51–59 (1996).
S. Liao, M. W. K. Law, and A. C. S. Chung, “Dominant local binary patterns for texture classification,” IEEE Trans. Image Process. 18 (5), 1107–1118 (2009).
A. Collignon, F. Maes, D. Delacre, D. Vandermeulen, P. Suentens, and G. Marchal, “Automated multi-modality image registration based on information theory,” in Proc. 14th Int. Conf. on Information Processing in Medical Imaging (Ile de Berder, France, 1995), Ser. Computational Imaging and Vision, Vol. 3 (Kluwer, Dordrecht, 1995), pp. 263–274.
M. P. Wachowiak, R. Smoliková, G. D. Tourassi, and A. S. Elmaghraby, “Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration,” in Medical Imaging 2003: Image Processing, Proc. SPIE 5032, 1090–1100 (2003).
J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “F-information measures in medical image registration,” IEEE Trans. Med. Imaging 23 (12), 1506–1518 (2004).
N. F. Rougon, C. Petitjean, and F. Prêteux, “Variational non rigid image registration using exclusive f-information,” in Proc. 2003 Int. Conf. on Image Processing (ICIP-2003) (Barcelona, Spain, 2003), IEEE, Vol. II, pp. II-703 – II-706.
M. Mellor and M. Brady, “Phase mutual information as a similarity measure for registration,” Med. Image Anal. 9 (4), 330–343 (2005).
L. Liu, T. Jiang, J. Yang, and C. Zhu, “Fingerprint registration by maximization of mutual information,” IEEE Trans. Image Process. 15 (5), 1100–1110 (2006).
D. G. Lowe, “Object recognition from local scale-invariant features,” in Proc. 7th IEEE Int. Conf. on Computer Vision (ICCV’99) (Kerkyra, Greece, 1999), Vol. 2, pp. 1150–1157.
M. S. Sarfraz and O. Hellwich, “Head pose estimation in face recognition across pose scenarios,” in Proc. Third Int. Conf. on Computer Vision Theory and Applications (VISAPP 2008) (Funchal, Madeira, Portugal, 2008), pp. 235–242 (2008).
H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Comput. Vision Image Understanding 110 (3), 346–359 (2008).
K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Trans. Pattern Anal. Mach. Intell. 27 (10), 1615–1630 (2005).
J. Yoo, S. S. Hwang, S. D. Kim, M. S. Ki, and J. Cha, “Scale-invariant template matching using histogram of dominant gradients,” Pattern Recogn. 47 (9), 3006–3018 (2014).
Y.-H. Lin and C.-H. Chen, “Template matching using the parametric template vector with translation rotation and scale invariance,” Pattern Recogn. 41 (7), 2413–2421 (2008).
K. Fredriksson, V. Makinen, and G. Navarro, “Rotation and lighting invariant template matching,” Inf. Comput. 205 (7), 1096–1113 (2007).
Y. Hel-Or, H. Hel-Or, and E. David, “Matching by tone mapping: Photometric invariant template matching,” IEEE Trans. Pattern Anal. Mach. Intell. 36 (2), 317–330 (2014).
G. Kovacs, “Matching by monotonic tone mapping,” IEEE Trans. Pattern Anal. Mach. Intell. 40 (6), 1424‒1436 (2018).
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error measurement to structural similarity,” IEEE Trans. Image Process. 13 (1), 1–14 (2004).
Yu. P. Pyt’ev and A. I. Chulichkov, Methods of Morphological Image Analysis (Fizmatlit, Moscow, 2010) [in Russian].
A. Goncharov, A. Gorban, A. Karkishchenko, and A. Lepskiy, “Content-based facial image search”, in Internet-Mathematics 2007: A Collection of Papers by Participants in the Competition of Scientific Projects in Information Search (Ural Fed. Univ., Ekaterinburg, 2007), pp. 56–64 [in Russian].
A. A. Gogol’, V. E. Dzhakoniya, et al., Television: A Textbook for Higher Schools, 2nd ed. (Radio i Svyaz’, Moscow, 2004) [in Russian].
Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. (Prentice Hall, Upper Saddle River, NJ, 2002; Tekhnosfera, Moscow, 2005).
Z. Wang, Rate Scalable Foveated Image and Video Communications, PhD thesis (The University of Texas, Austin, 2001).
L. Wang, Y. Zhang, and J. Feng, “On the Euclidean distance of images,” IEEE Trans. Pattern Anal. Mach. Intell. 27 (8), 1334–1339 (2005).
F. Tang, S. H. Lim, N. L. Chang, and H. Tao, “A novel feature descriptor invariant to complex brightness changes”, in Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Miami, FL, June 20-25, 2009), pp. 2631–2638.
B. G. Litvak, Expert Inofrmation: Methods of Acquisition and Analysis (Radio i Svyaz’, Moscow, 1982) [in Russian].
A. G. Bronevich and A. N. Karkishchenko, “A family of proximity measures for expert estimates, and the choice of an optimal measure,” Autom. Remote Control 55 (4), Part 2, 568–575 (1994).
A. G. Bronevich, A. N. Karkishchenko, and A. E. Lepskiy, Analysis of the uncertainty of informative feature extraction and image representations (Fizmatlit, Moscow, 2013) [in Russian].
A. N. Karkishchenko and A. V, Goncharov, “Investigation of the stability of the sign representation of images,” Autom. Remote Control 71 (9), 1793–1803 (2010).
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This study was supported by the Russian Foundation for Basic Researches, projects no. 19-07-00873 and 17-20-02017.
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Karkishchenko Alexander Nikolaevich was born in 1956. In 1978 he graduated from Taganrog State University of Radio Engineering, specialty “Applied Mathematics”. He received the degree of Candidate of Sciences in 1983, and Doctor of Sciences in 1997. Now he is a Professor at Southern Federal University. Field of interests: graph theory; combinatorial analysis, theory of possibilities, theory of nonadditive measures, mathematical models for classification, image processing and analysis, pattern recognition. He is the author of more than 200 scientific papers.
Mnukhin Valeriy Borisovich was born in 1958. In 1979 he graduated from Taganrog State University of Radio Engineering, specialty “Applied Mathematics”. He received the degree of Candidate of Sciences from the Institute of Mathematics with the Computing Center of the Academy of Sciences of the Moldavian SSR in 1985. Now he is an Associate Professor at Southern Federal University. Field of interests: mathematical methods for pattern recognition, algebraic and topological combinatorial analysis, problems of graph reconstruction, spectral graph theory. He is the author of more than 80 scientific papers.
Translated by Yu. Zikeeva
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Karkishchenko, A.N., Mnukhin, V.B. On the Metric on Images Invariant with Respect to the Monotonic Brightness Transformation. Pattern Recognit. Image Anal. 30, 359–371 (2020). https://doi.org/10.1134/S1054661820030104
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DOI: https://doi.org/10.1134/S1054661820030104