G. L. Gimel'farb, "Texture modeling by multiple pairwise pixel interactions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 11, pp. 1110--1114, Nov. 1996. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Strong Markov Random Field Model - Paget (2004) (Correct)
....distribution for a particular MRF, the neighborhood system needs to be known, and the potential functions for each clique are required. Apart from a simple binary automodel [20] there is no exact solution to these parameters. In which case, a maximum likelihood estimation (MLE) is required [9], 19] The process to estimate the maximum likelihood is to first choose a parameterized automodel [9] and then perform updates on those parameters with respect to the MRF that the parameterized model generates. The model is slowly refined until the marginal distributions of the generated field ....
....for each clique are required. Apart from a simple binary automodel [20] there is no exact solution to these parameters. In which case, a maximum likelihood estimation (MLE) is required [9] 19] The process to estimate the maximum likelihood is to first choose a parameterized automodel [9], and then perform updates on those parameters with respect to the MRF that the parameterized model generates. The model is slowly refined until the marginal distributions of the generated field is comparable with the equivalent distributions of the training field. An alternative approach, as ....
G. L. Gimel'farb. Texture modeling by multiple pairwise pixel interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(11):1110--1114, Nov. 1996.
Strong Markov Random Field Model - Paget (Correct)
....for a particular Markov random field, the neighbourhood system needs to be known, and the potential functions for each clique are required. Apart from a simple binary auto model [31] there is no exact solution to these parameters. In which case, a maximum likelihood estimation is required [14, 30]. The process involved to estimate maximum likelihood is to first choose a parameterised auto model [14] and then perform updates on those parameters with respect to the Markov random field that the parameterised model generates. The model is slowly refined until the marginal distribution ....
....for each clique are required. Apart from a simple binary auto model [31] there is no exact solution to these parameters. In which case, a maximum likelihood estimation is required [14, 30] The process involved to estimate maximum likelihood is to first choose a parameterised auto model [14], and then perform updates on those parameters with respect to the Markov random field that the parameterised model generates. The model is slowly refined until the marginal distribution functions of the generated fields are comparable with the equivalent functions taken from the training data. ....
G. L. Gimel'farb. Texture modeling by multiple pairwise pixel interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(11):1110--1114, Nov. 1996.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE.. - Rupert Paget Member (2004) (Correct)
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G. L. Gimel'farb, "Texture modeling by multiple pairwise pixel interactions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 11, pp. 1110--1114, Nov. 1996.
Fields of Experts: A Framework for Learning Image Priors - Roth, Black (2005) (Correct)
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G. Gimel'farb. Texture modeling by multiple pairwise pixel interactions. PAM I , 18(11):1110--1114, 1996.
Fields of Experts: A Framework for Learning Image Priors - Roth, Black (2005) (Correct)
No context found.
G. Gimel'farb. Texture modeling by multiple pairwise pixel interactions. PAMI, 18(11):1110--1114, 1996.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE.. - Rupert Paget Member (2004) (Correct)
No context found.
G. L. Gimel'farb, "Texture modeling by multiple pairwise pixel interactions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 11, pp. 1110--1114, Nov. 1996.
Illumination Invariant and Occlusion Robust Vehicle Tracking .. - KAMIJO, SAKAUCHI (2002) (1 citation) (Correct)
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G.L.Gimel'farb, "Texture Modeling by Multiple Pairwise Pixel Interactions", IEEE trans. PAMI, Vol.18, No.11, 1996, pp1110-1114.
Statistical Modeling for Motion-Based Video Classification.. - Fablet, Bouthemy (2001) (Correct)
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G.L. Gimel'Farb. Texture modeling by multiple pairwise pixel interactions. IEEE Trans. on Pattern Analysis and Machine Intelligence, 18(11):1110--1114, 1996.
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