Titlebook: Image Segmentation and Compression Using Hidden Markov Models; Jia Li,Robert M. Gray Book 2000 Springer Science+Business Media New York 20

允许

Statistical Classification,enabling closely related topics to be studied efficiently. We restrict our interest to scientific classification, which is about discovering the optimal rule to classify a set of objects with respect to their true classes. Techniques for classification originated from biological taxonomy, dating bac

漂泊

Vector Quantization,ate for a communication system while retaining the best allowable fidelity to the original. Since the use of resources such as bandwidth will generally expand to meet the resources available, it will always be beneficial to apply data compression even though the cost of bandwidth and storage capacit

BUOY

Two Dimensional Hidden Markov Model,. Choosing block sizes is consequently critical. We do not want to choose a block size too large since this obviously entails crude classification. On the other hand, if we choose a small block size, only very local properties belonging to the small block are examined in classification. The penalty

系列

puzzle

Testing Models,ourselves how accurate the models are. It is obvious that the potential of the algorithms described in Chapter 4 and 5 ought to depend to some extent upon the validity of the hidden Markov mod els. Although good results are achieved by algorithms based on the HMMs, which intuitively justify the mode

不自然

arthroplasty

Harbor

偶然

Parabola

Testing Models,odels is to balance their accuracy and computational complexity; that they are absolutely correct is not really an issue. The purpose of testing is thus more for gaining insight into how improvements can be made rather than for arguing the literal truthfulness of the models.