-
1
-
-
3543081155
-
-
Ph.D. thesis, The Gatsby Computational Neuroscience Unit, University College London, London, UK
-
M. J. Beal, Variational Algorithms for Approximate Bayesian Inference, Ph.D. thesis, The Gatsby Computational Neuroscience Unit, University College London, London, UK (2003).
-
(2003)
Variational Algorithms for Approximate Bayesian Inference
-
-
Beal, M.J.1
-
3
-
-
0032587846
-
An information-theoretic approach to interactions in images
-
G. Boccignone and M. Ferraro, An information-theoretic approach to interactions in images, Spat. Vis. 12 (1999) 345-362.
-
(1999)
Spat. Vis
, vol.12
, pp. 345-362
-
-
Boccignone, G.1
Ferraro, M.2
-
5
-
-
49949092341
-
A variational Bayes approach to image segmentation
-
Advances in Brain, Vision, and Artificial Intelligence. Second Int. Symp, eds. F. Mele, G. Ramella, S. Santillo and F. Ventriglia
-
G. Boccignone, M. Ferraro and P. Napoletano, A variational Bayes approach to image segmentation, Advances in Brain, Vision, and Artificial Intelligence. Second Int. Symp., eds. F. Mele, G. Ramella, S. Santillo and F. Ventriglia, Lecture Notes in Computer Science, Vol. 4729 (2007), pp. 234-243.
-
(2007)
Lecture Notes in Computer Science
, vol.4729
, pp. 234-243
-
-
Boccignone, G.1
Ferraro, M.2
Napoletano, P.3
-
6
-
-
33746263998
-
Probabilistic models of cognition: Conceptual foundations
-
N. Chater, J. B. Tenenbaum and A. Yuille, Probabilistic models of cognition: conceptual foundations, Trends Cogn. Sci. 10 (2006) 287-291.
-
(2006)
Trends Cogn. Sci
, vol.10
, pp. 287-291
-
-
Chater, N.1
Tenenbaum, J.B.2
Yuille, A.3
-
8
-
-
0033220879
-
On the representation of image structures via scale-space entropy conditions
-
M. Ferraro, G. Boccignone and T. Caelli, On the representation of image structures via scale-space entropy conditions, IEEE Trans. Patt. Anal. Mach. Intell. 21 (1999) 1199-1203
-
(1999)
IEEE Trans. Patt. Anal. Mach. Intell
, vol.21
, pp. 1199-1203
-
-
Ferraro, M.1
Boccignone, G.2
Caelli, T.3
-
10
-
-
25844482570
-
A comparison of algorithms for inference and learning in probabilistic graphical models
-
B. J. Frey and N. Jojic, A comparison of algorithms for inference and learning in probabilistic graphical models, IEEE Trans. Patt. Anal. Mach. Intell. 27 (2005) 1392-1416.
-
(2005)
IEEE Trans. Patt. Anal. Mach. Intell
, vol.27
, pp. 1392-1416
-
-
Frey, B.J.1
Jojic, N.2
-
11
-
-
0032122746
-
Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm
-
S. S. Gopal and T. J. Hebert, Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm, IEEE Trans. Image Proc. 7 (1998) 1014-1028.
-
(1998)
IEEE Trans. Image Proc
, vol.7
, pp. 1014-1028
-
-
Gopal, S.S.1
Hebert, T.J.2
-
12
-
-
0029519825
-
Quantitative methods of evaluating image segmentation
-
Q. Huang and B. Dom, Quantitative methods of evaluating image segmentation, IEEE Int. Conf. Image Processing 3 (1995) 53-56.
-
(1995)
IEEE Int. Conf. Image Processing
, vol.3
, pp. 53-56
-
-
Huang, Q.1
Dom, B.2
-
16
-
-
33746813525
-
Variational learning for Gaussian mixture models
-
N. Nasios and A. G. Bors, Variational learning for Gaussian mixture models, IEEE Trans. Syst. Man Cybern.-B 36 (2006) 849-862.
-
(2006)
IEEE Trans. Syst. Man Cybern.-B
, vol.36
, pp. 849-862
-
-
Nasios, N.1
Bors, A.G.2
-
17
-
-
26144467412
-
Variational Bayes for d-dimensional Gaussian mixture models
-
Technical Report, Wellcome Department of Cognitive Neurology, University College, London, UK
-
W. Penny, Variational Bayes for d-dimensional Gaussian mixture models, Technical Report, Wellcome Department of Cognitive Neurology, University College, London, UK (2001).
-
(2001)
-
-
Penny, W.1
-
18
-
-
0036566199
-
Image segmentation by data-driven Markov chain Monte Carlo
-
Z. Tu and S.-C. Zhu, Image segmentation by data-driven Markov chain Monte Carlo, IEEE Trans. Patt. Anal. Mach. Intell. 24 (2002) 657-673.
-
(2002)
IEEE Trans. Patt. Anal. Mach. Intell
, vol.24
, pp. 657-673
-
-
Tu, Z.1
Zhu, S.-C.2
-
20
-
-
0002023307
-
Applications of nonlinear diffusion in image processing and computer vision
-
J. Weickert, Applications of nonlinear diffusion in image processing and computer vision, Acta Math. Univ. Comenianae 70 (2001) 33-50.
-
(2001)
Acta Math. Univ. Comenianae
, vol.70
, pp. 33-50
-
-
Weickert, J.1
-
22
-
-
33947193059
-
Variational Bayes inference of spatial mixture models for segmentation
-
M. W. Woolrich and T. E. Behrens, Variational Bayes inference of spatial mixture models for segmentation, IEEE Trans. Med. Imag. 25 (2006) 1380-1391.
-
(2006)
IEEE Trans. Med. Imag
, vol.25
, pp. 1380-1391
-
-
Woolrich, M.W.1
Behrens, T.E.2
-
23
-
-
0034745001
-
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
-
Y. Zhang, M. Brady and S. Smith, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm, IEEE Trans. Med. Imag. 20 (2001) 45-57.
-
(2001)
IEEE Trans. Med. Imag
, vol.20
, pp. 45-57
-
-
Zhang, Y.1
Brady, M.2
Smith, S.3
|