A Markov random field based hybrid algorithm with simulated annealing and genetic algorithm for image segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4221 LNCS - I, Issue , 2006, Pages 706-715

  Du, Xinyu ^a   Li, Yongjie ^a   Chen, Wufan ^a   Zhang, Yi ^a   Yao, Dezhong ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; GENETIC ALGORITHMS; IMAGE SEGMENTATION; SIMULATED ANNEALING;

HYBRID ALGORITHMS; IMAGE DE-NOISING; MARKOV RANDOM FIELD (MRF); METROPOLIS ALGORITHMS;

MARKOV PROCESSES;

EID: 33750335895     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11881070_95     Document Type: Conference Paper

References (17)

1. Wesley, S., Hairong, Q.: Machine Vision. Cambridge University Press (2004)
2. Tan, Y.P., Yap, K.H., Wang, L.P. (Eds.): Intelligent Multimedia Processing with Soft Computing. Springer, Berlin Heidelberg New York (2004)
3. Stan, Li.: Markov Random Field Modeling in Image Analysis. Springer, Tokyo, Japan (2001)
4. Karvonen, J.A.: Baltic Sea ice SAR segmentation and classification using modified pulsecoupled neural networks. IEEE Trans. Geosciences and Remote Sensing, 42 (2004) 1566-1574
5. Stuart, G., Donald, G.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans.on Pattern Analysis and Machine Intelligence, Vol. 6, No. 6 (1984) 721-741
6. Halgamuge, S., Wang, L.P. (eds.), Classification and Clustering for Knowledge Discovery, Springer, Berlin (2005)
7. Xiao, W., Han, W.: Evolutionary Optimization with Markov Random Field Prior. IEEE Trans.on Evolutionary Computation, Vol. 8, No 6 (2004) 567-579
8. Dinchang, T., Chihching, L.: A Genetic Algorithm for MRF-based Segmentation of Multispectral Textured Images. Patter Recognition Letters 20 (1999) 1499-1510
9. Suchendra, M, B., Hui, Z.: Image Segmentation Using Evolutionary Computation. IEEE Tran.on Evolutionary Computation Vol. 3, No. 1(1999)1-21
10. Salima, O., Mohamed, B,: MRF-based Images Segmentation Using Ant Colony System. Electronic Letters on Computer Vision and Image Analysis 2(2) (2003) 12-24
11. Jinhao, X,. Yujin, Z., Xinggang, L.: Dynamic Image Segmentation Using 2-D Genetic Algorithms. ACTA AUTOMATICA SINICA Vol.26, No.5 (2000) 685-689
12. Yanqiu, F., Wufan, C.: A New Algorithm for Image Segmentation Based on Gibbs Random Field and Fuzzy C-Means clustering. ACTA ELECTRONICA SINICA. Vol32, No.4 (2004) 645-647
13. Lahlou, A., Wojciech, P.: Hierarchical Markov Fields and Fuzzy Image Segmentation. Second IEEE International Conference on Intelligent Processing Systems (ICIPC'98). Gold Coast, Australia, August, 4-7, 1998
14. Hu, R., Fahmy, M, M.: Texture Segmentation based on a hierachical MRF model. Signal processing. 26 (1992) 285-305
15. Zhaobao, Z.: Markov Random Field Method for Image Analysis. Wuhan Measurement Technology University Press (2000)
16. http://www.cma.mgh.harvard.edu/ibsr/
17. Dembele, D., Kastner, P.: Fuzzy C-Means Method for Clustering Microarry Data. Bioinformatics, Vol.19, No.8 (2003)973-980