Stochastic Models, Statistical Methods, and Algorithms in Image Analysis de Piero Barone

Stochastic Models, Statistical Methods, and Algorithms in Image Analysis: Proceedings of the Special Year on Image Analysis, held in Rome, Italy, 1990: Lecture Notes in Statistics, cartea 74

Piero Barone

Arnoldo Frigessi

Mauro Piccioni

en Limba Engleză Paperback – 24 iun 1992

This volume comprises a collection of papers by world- renowned experts on image analysis. The papers range from survey articles to research papers, and from theoretical topics such as simulated annealing through to applied image reconstruction. It covers applications as diverse as biomedicine, astronomy, and geophysics. As a result, any researcher working on image analysis will find this book provides an up-to-date overview of the field and in addition, the extensive bibliographies will make this a useful reference.

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Cuprins

1. Edge Preserving Image Restoration.- 2. Boltzmann Machines: High-Order Interactions and Synchronous Learning.- 3. Bayesian 3-D Path Search and Its Applications to Focusing Seismic Data.- 4. Edge Detection and Segmentation of Textured Plane Images.- 5. IFS Algorithms for Wavelet Transforms, Curves and Surfaces, and Image Compression.- 6. Image Restoration by Stochastic Dichotomic Reconstruction of Contour Lines.- 7. A Comparison of Simulated Annealing of Gibbs Sampler and Metropolis Algorithms.- 8. Some Limit Theorems on Simulated Annealing.- 9. Statistical Analysis of Markov Random Fields Using Large Deviation Estimates.- 10. Metropolis Methods, Gaussian Proposals and Antithetic Variables.- 11. The Chi-Square Coding Test for Nested Markov Random Field Hypotheses.- 12. Asymptotic Comparison of Estimators in the Ising Model.- 13. A Remark on the Ergodicity of Systematic Sweep in Stochastic Relaxation.- 14. Application of Bayesian Methods to Segmentation in Medical Images.- 15. Some Suggestions for Transmission Tomography Based on the EM Algorithm.- 16. Deconvolution in Optical Astronomy. A Bayesian Approach.- 17. Parameter Estimation for Imperfectly Observed Gibbs Fields and Some Comments on Chalmond’s EM Gibbsian Algorithm.