4 Applications of Pattern Recognition Technology

The chapter presents several techniques for preprocessing or conditioning the input data and presents a specific algorithm that provides for the specification and evaluation of features as the classifier is being designed.

Abstract

Pattern recognition is concerned with the investigation of adaptive and analytic techniques for processing large amounts of data, the extraction of useful information to reduce the data, and the classification of the data as required. The chapter presents several techniques for preprocessing or conditioning the input data. The purpose of signal conditioning or “preprocessing,” is to provide a convenient input format to provide invariance, to provide in many cases a reduction in the dimensionality of the input data, and to emphasize aspects of the input signals that are important. Preprocessing includes techniques such as scanning, edge enhancement, Fourier transformation, and autocorrelation. The chapter also discusses several techniques for extracting features from the conditioned data and for the design of the classifier and presents a specific algorithm that provides for the specification and evaluation of features as the classifier is being designed. Finally, several applications are presented in the chapter to demonstrate how the mechanisms of pattern recognition technology can be utilized to classify data resulting from a variety of sensory mechanisms.