Analysis of 2D problem in HMM

• Autorzy: Bobulski, J.
• Języki publikacji: EN

Abstrakty

EN

Hidden Markov models are widely applying in data classification. They are using in many areas where 1D data are processing. In the case of 2D data, appear some problems with applying 2D HMM. This paper describe the important limitations of HMM when we have to processing two dimensional data.

Słowa kluczowe

EN

image processing; hidden Markov models HMM; pseudo 2DHMM; 2DHMM

• Czasopismo: Metody Informatyki Stosowanej
• Rocznik: 2008
• Tom: nr 4 (Tom 17)
• Strony: 27--33
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

Bobulski, J.

• Czestochowa University of Technology, Institute of Computer and Information Science

Bibliografia

• [1] Kanungo T. Hidden Markov Model Tutorial. www.cfar.umd.edu/~kanungo
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• [3] Rabiner L. R. A tutorial on hidden Markov models and selected application in speech recognition. Proc. IEEE 77, 1989, pp. 257
• [4] Hu J., Brown M. K., Turin W. HMM Based On IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 18 No.10 (1996) 1039-1045
• [5] Forney G. D. The Viterbi Algorithm, Proc. IEEE, Vol. 61 No. 3 (1973) 268
• [6] Eickeler S., Müller S., Rigoll G. High Performance Face Recognition Using Pseudo 2-D Hidden Markov Models, European Control Conference, 1999
• [7] Eickeler S., Müller S., Rigoll G. Recognition of JPEG compresed faceimages based on statistical methods, Image and Vision Computing 18(2000), pp. 279
• [8] Li J., Najmi A., Gray R. M. Image classification by a two dimensional Hidden Markov model, IEEE Trans. Signal Process. 48(2000), pp. 517--533.