论文信息 - Mathematical statistics and computer vision - 字舞流文

Mathematical statistics and computer vision

In this discussion paper, I present my views on the role on mathematical statistics for solving computer vision problems.

Rama Chellappa | R. Chellappa

[1] W. Larimore,et al. Statistical inference on stationary random fields , 1977, Proceedings of the IEEE.

[2] Richard Szeliski,et al. A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3] J. Besag. On the Statistical Analysis of Dirty Pictures , 1986 .

[4] J. Besag. Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[5] Stochastic Relaxation , 2014, Computer Vision, A Reference Guide.

[6] G. B. Smith,et al. Preface to S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images” , 1987 .

[7] Y. Rozanov,et al. On the Densities of Gaussian Distributions and Wiener-Hopf Integral Equations , 1966 .

[8] John W. Woods,et al. Two-dimensional discrete Markovian fields , 1972, IEEE Trans. Inf. Theory.

[9] Yu. A. Rosanov. On Gaussian Fields with Given Conditional Distributions , 1967 .

[10] J. Besag,et al. On the estimation and testing of spatial interaction in Gaussian lattice processes , 1975 .

[11] H. Elliott,et al. Stochastic boundary estimation and object recognition , 1980 .

[12] P. Whittle. ON STATIONARY PROCESSES IN THE PLANE , 1954 .

[13] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.