Commenced in January 2007
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Edition: International
Paper Count: 33122
A Selective Markovianity Approach for Image Segmentation
Authors: A. Melouah, H. Merouani
Abstract:
A new Markovianity approach is introduced in this paper. This approach reduces the response time of classic Markov Random Fields approach. First, one region is determinated by a clustering technique. Then, this region is excluded from the study. The remaining pixel form the study zone and they are selected for a Markovianity segmentation task. With Selective Markovianity approach, segmentation process is faster than classic one.Keywords: Markovianity, response time, segmentation, study zone.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075679
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[1] Gregory A. Baxes, Digital Image Processing principles and applications, John Wiley & Sons, Inc 1994.
[2] M. Cheriet, J. N. Said, and C. Y. Suen, "A Recursive Thresholding Technique for Image Segmentation", IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 6, JUNE 1998
[3] Sylvie Fhilipp-Foliguet, " Segmentation d'images en régions floues ", Logique Floue et Applications, LFA 2000, La Rochelle, 2000.
[4] S. Geman and D. Geman, "Stochastic relaxation, gibbs distribution, and the bayesian restoration of image", IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721{741, 1984.
[5] S.Z. Li, Markov Random Field Modelling in Image Analysis, Springer: Tokyo, Japan, 2001.
[6] S. Z. Li, Markov Random Field Modeling in Computer Vision, Springer: Edt.Tosiyasu L.Kunii, 1995.
[7] J. MacQueen,." Some methods for classification and analysis of multivariate observations", In Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281-297, 1967.
[8] A. Nazif and M.D. Levine, "Low-level image segmentation: an expert system". IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(5):555{577, september 1984.
[9] S. Wesolkowski, P. Fieguth, "Adaptive Color Image Segmentation Using Markov Random Fields", IEEE ICIP 2002.
[10] S. Wesolkowski, P. Fieguth, "A Probabilistic Framework for Image Segmentation", IEEE ICIP 2003.