ABSTRACT
Chapter Overview This chapter addresses the particularly relevant issues of pattern recognition, classification and clustering, concentrating on the perspective of shape classification. The common element characterizing each of these approaches is the task of assigning a specific class to some observed individual, with basis on a selected set of measures. One of the most important steps in pattern recognition is the selection of an appropriate set of features with good discriminative power. Such measures can then be normalized by using statistical transformations, and fed to a classification algorithm. Despite the decades of intense research in pattern recognition, there are no definitive and general solutions to choosing the optimal features and obtaining an optimal classification algorithm. Two main types of classification approaches are usually identified in the literature: supervised and unsupervised, which are characterized by the availability of prototype objects. In this chapter we present, discuss and illustrate two techniques representative of each of these two main types of classification approaches.
