Bayesian entropy estimation applied to non-gaussian robust image segmentation

[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.