Improving the noise immunity of the mean shift method when segmenting color images
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Abstract
The modified mean shift method of color image segmentation is elaborated. It based on the many-stage more precise definition of the cluster centers in the feature space with the help of the adapted choice of the Parsen window for nonparametric estimation. This is allow to segment color images with high noise stability
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References
R. Gonzalez and R. Woods, Digital processing of images, Moscow: Tekhnosfera, 2005, p. 1072.
M. Herbin, N. Bonnet, and P. Vautrot, “A clustering method based on the estimation of the probability density function and on the skeleton by influence zones. Application to image processing”, Pattern Recognition Letters, vol. 17, no. 11, pp. 1141–1150, Sep. 1996. DOI:10.1016/0167-8655(96)00085-2
I. Mandel, Cluster analysis, Moscow: Finance and statistics, 1988, p. 176.
A. Touzani and J.-G. Postaire, “Clustering by mode boundary detection”, Pattern Recognition Letters, vol. 9, no. 1, pp. 1–12, Jan. 1989. DOI:10.1016/0167-8655(89)90022-6
R. Wilson and M. Spann, “A new approach to clustering”, Pattern Recognition, vol. 23, no. 12, pp. 1413–1425, Jan. 1990. DOI:10.1016/0031-3203(90)90087-2
D. Comaniciu, “An algorithm for data-driven bandwidth selection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 2, pp. 281–288, Feb. 2003. DOI:10.1109/TPAMI.2003.1177159
E. Stolnits, T. DeRose, and D. Salesin, Wavelets in computer graphics, Izhevsk: NITs RKhD, 2002, p. 272.
D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, May 2002. DOI:10.1109/34.1000236
M. Aoki, Introduction to Optimization Methods: Fundamentals and Applications of Nonlinear Programming, Moscow: Nauka, 1977, p. 344.
R. Duda and P. Hart, Pattern Recognition and Scene Analysis, Moscow: Mir, 1978, p. 510.
W. Pratt and D. Lebedev, Digital Image Processing: In 2 volumes, vol. 2. Moscow: Mir, 1982, p. 790.
V. Abakumov, V. Krylov, and S. Antoshchuk, “Increasing the efficiency of processing image information in automatedsystems”, Electronics and Communications, no. Part 1, pp. 100–105, 2005.



