Abstract
The image processing techniques introduced in the previous chapter optimize heuristic criteria. This method of algorithm design presents some advantages. It makes it possible to introduce very naturally additional constraints, such as regularization terms, and to handle large training sets in an efficient way. However, there is no proof that these algorithms optimize the parameters which are of interest for the user. These parameters may be the probability of recognition for classification applications, estimator variance for angle estimation, or detection probability for detection tasks. The appropriate framework for designing image processing algorithms which optimize such parameters is statistical decision and estimation theory.
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