7.2.4. Parametric Models
So far, in various parts of the book, we have treated the gray levels as random variables and looked at aspects of their first- and second-order statistics. In this subsection, their randomness will be approached from a different perspective. We will assume that I(m,n) is a real nondiscrete random variable, and we will try to model its underlying generation mechanism by adopting an appropriate parametric model. The parameters of the resulting models encode useful information and lend themselves as powerful feature candidates for a number of pattern recognition tasks.
We will move in two directions. One is to treat an image as a successive sequence of rows or columns. That is, our random variables will be considered ...
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