Titlebook: Markov Random Field Modeling in Computer Vision; S. Z. Li Book 19951st edition Springer Japan 1995 Markov Random Field.Markov random field

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Cacophonous

Anal-Canal

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 1995e) to improve the stability of the robust M-estimator to the initialization.

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MRF Parameter Estimation,on, estimating the involved parameters has a much shorter history. Generally, it is performed by optimizing a statistical criterion, . using existing techniques such as maximum likelihood, coding, pseudo-likelihood, expectation-maximization, Bayes.

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Discontinuities in MRFs, Markov random fields (MRFs) (Geman and Geman 1984; Elliott et al. 1984; Marroquin 1985) and regularization (Poggio et al. 1985). Its applications are seen widely in image restoration, surface reconstruction, optical flow and motion, shape from X, texture, edge detection, region segmentation, visual integration and so on.

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second edition of?.Metastasis Research Protocols,?.brings together updated versions of the seminal technique that were presented in the first edition and also includes new techniques that have recently been shown to be important in illuminating the processes underlying this important area of biolog

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S. Z. Lisity of carbohydrate-binding preferences. They can be used in the laboratory to detect carbohydrate structures on, or in, cells and tissues in much the same way that purified antibodies can be employed to detect cell- or tissue-bound antigens using immunocytochemistry. As lectins can distinguish sub

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S. Z. Liarticularly useful for the assessment of two key members of the matrix metalloproteinase family, MMP-2 (gelatinase A) and MMP-9 (gelatinase B), due to their potent gelatin-degrading activity. This polyacrylamide gel electrophoresis-based method can provide a reliable assessment of the type of gelati