Titlebook: Markov Random Field Modeling in Image Analysis; Stan Z. Li Book 20012nd edition Springer Japan 2001 Excel.Markov Random Field.Markov model

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過份艷麗

gene-therapy

,Minimization — Local Methods,ry diffi-cult in vision problems due to the complexity caused by interactions between labels. Therefore, optimal solutions are usually computed by using some it-erative search techniques. This chapter describes techniques for finding local minima and discusses related issues.

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1431-1488 s for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers t

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Optic-Disk

Discontinuity-Adaptivity Model and Robust Estimation,previous chapter. This chapter provides a comparative study (Li 1995a) of the two kinds of models based on the results about the DA model and presents an algorithm (Li 1996b) to improve the stability of the robust M-estimator to the initialization.

改變

圖畫文字

Introduction, the optimal solution to a vision problem and how to find the optimal solution. The reason for defining the solution in an . sense is due to various uncertainties in vision processes. It may be difficult to find the perfect solution, so we usually look for an optimal one in the sense that an objective in which constraints are encoded is optimized.

職業(yè)

,Minimization — Global Methods,l if the energy function contains many local minima. Whereas methods for local minimization are quite mature with commercial software on market, the study of global minimization is still young. There are no efficient algorithms which guarantee to find globally minimal solutions as are there for local minimization.