Bayesian entropy estimation applied to non-gaussian robust image segmentation
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[1] Luc Pronzato,et al. A minimum-entropy estimator for regression problems with unknown distribution of observation errors , 2001 .
[2] Bahram Javidi,et al. Speckle removal using a maximum-likelihood technique with isoline gray-level regularization. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.
[3] Xiao-Gang Lei,et al. Fast segmentation approach for SAR image based on simple Markov random field , 2010 .
[4] G. Terrell. The Maximal Smoothing Principle in Density Estimation , 1990 .
[5] Richard Szeliski,et al. Bayesian modeling of uncertainty in low-level vision , 2011, International Journal of Computer Vision.
[6] Ken D. Sauer,et al. A generalized Gaussian image model for edge-preserving MAP estimation , 1993, IEEE Trans. Image Process..
[7] Tomaso Poggio,et al. Probabilistic Solution of Ill-Posed Problems in Computational Vision , 1987 .
[8] Mariano Rivera,et al. Entropy-Controlled Quadratic Markov Measure Field Models for Efficient Image Segmentation , 2007, IEEE Transactions on Image Processing.
[9] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[10] L. Devroye. A Note on the Usefulness of Superkernels in Density Estimation , 1992 .
[11] M. C. Jones,et al. Universal smoothing factor selection in density estimation: theory and practice , 1997 .
[12] Gilles Fleury,et al. Minimum-entropy, PDF approximation, and kernel selection for measurement estimation , 2003, IEEE Trans. Instrum. Meas..
[13] Rama Chellappa,et al. Multiresolution Gauss-Markov random field models for texture segmentation , 1997, IEEE Trans. Image Process..
[14] David A. Clausi,et al. Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[15] José Ismael de la Rosa Vargas,et al. Markovian Random Fields and Comparison Between Different Convex Criterion , 2007, 17th International Conference on Electronics, Communications and Computers (CONIELECOMP'07).
[16] Jesús Villa,et al. Semi-Huber potential function for image segmentation. , 2012, Optics express.