<title>Template basis techniques for pattern recognition</title>

Abstract

The purpose of this paper is to present an approach to pattern recognition which acknowledges theories in the fields of perception, the human visual system and decomposition. It is hoped that by taking a panoramic view of the subject matter that insights into the issue may uncover a possible course of action. At the very least, there should be evidence that certain tools used in the process adequately fit the analysis of the problem. The contemporary perceptual theory to classification recognizes the fundamental concepts of scale and localization. The visual system can solve problems that would be intractable using a single depiction of a scene by having access to representations at different spatial scales. Enhancement, analysis and compression are the areas of image processing most germane to pattern recognition. And the simplest statistical approach to identifying patterns utilizes templates. All these properties can be inherently exploited in the wavelet domain. By unifying the process of noise reduction, segmentation, feature extraction and classification it is possible to develop a general technique which might not be optimal but has the advantage of being computationally efficient. All the desired properties that are required in an analytic task of this nature are captured with multiresolution analysis.