16 Structural methods in image analysis and recognition

TLDR: This chapter focuses on structural methods in image analysis and recognition, which allow the designer or the user of a pattern recognition system to employ a somewhat intuitive description of an object as the basis for a recognition scheme.

Abstract: Publisher Summary A wide variety of techniques has been developed for image analysis. One common approach consists of image segmentation using some type of similarity criterion for grouping areas within an image followed by measurement of resulting region properties such as shape and texture. These measurements are used to classify the regions into types by computing the similarity of these measurements to those of a set of tracking regions. Statistical methods such as discriminant analysis and the Bayesian classifiers have been used for this classification step. This chapter focuses on structural methods in image analysis and recognition. The primary goal of structural pattern recognition procedures has been the recognition of objects in an image. Structural methods are appealing because they allow the designer or the user of a pattern recognition system to employ a somewhat intuitive description of an object as the basis for a recognition scheme. A fundamental part of many structural recognition systems is a search space. This space may be explicitly stored in a computer or implicitly stored and dynamically generated as in a grammar. Often measures of merit are defined on those parts of the search space that have been examined.