Belief Propagation and Potts Model

• Autorzy: Gwizdałła T.M.
• Konferencja: Evolutionary Computation and Global Optimization 2009 / National Conference (12 ; 1-3.06.2009 ; Zawoja, Poland)
• Języki publikacji: EN

Abstrakty

EN

In the paper there are presented the results of steady state searching for 3-5 state Potts model. The results of simulation are presented and its efficiency is discussed. Finally the model is adapted to the trial of reconstruction of grey scaled images initially blurred by the special modification procedure. The results show the usefulness of the simple method in the process of contour recognition.

Słowa kluczowe

EN

Potts model; Loopy Belief Propagation algorithm; image analysis; contour recognition; evolutionary algorithm; evolutionary computation

PL

model Pottsa; analiza obrazu; rozpoznawanie konturów; algorytm ewolucyjny; obliczenia ewolucyjne

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2009
• Tom: z. 169
• Strony: 79--86
• Opis fizyczny: Bibliogr. 10 poz., rys., wykr.

Twórcy

autor

Gwizdałła T.M.

• University of Łodź, Dept. of Solid State Physics, Pomorska 149/153, 90-236 Łodź, Poland,

Bibliografia

• [1] Michael Chertkov, Vladimir Y. Chernyak, and Razvan Teodorescu. Belief propagation and loop series on planar graphs. Journal of Statistical Mechanics: Theory and Experiment, 2008(05):P05003 (19pp), 2008.
• [2] P.P. Felzenszwalb and D.P. Huttenlocher. Efficient Belief Propagation for early vision. International Journal of Computer Vision, 70:41-53, 2006.
• [3] S. Guan and R. Klette. Belief-propagation on edge images for stereo analysis of image sequences. In Lecture Notes In Computer Science; Vol. 4931/2008, Robot Vision, pages 291-302. Springer-Verlag, 2008.
• [4] T.M. Gwizdałła. The critical properties of magnetic system using Generalized Belief Propagation technique. In Proceedings of XI KAEiOG Conference, Szymbark, pages 85-94, 2008.
• [5] M.B. Hastings. Community detection as an inference problem. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 74(3):035102, 2006.
• [6] I. Nwogu and J.J. Corso. (BP)2: Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1-8, 2008.
• [7] R.B. Potts. Some generalized order-disorder transformations. Proc. Cambr. Phil. Soc., 48:106-109, 1952.
• [8] Kazuyuki Tanaka and D.M. Titterington. First-order phase transition and Bayesian Image Processing by Loopy Belief Propagation. Progress of Theoretical Physics Supplement, 157:288-291, 2005.
• [9] J.S. Yedidia, W.T. Freeman, and Y. Weiss. Bethe free energy, Kikuchi approximations and belief propagation algorithms. In MERL Report, 2000.
• [10] Zhaozheng Yin and Robert Collins. Belief Propagation in a 3D Spatio-temporal MRF for Moving Object Detection. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 0:1-8, 2007.