Pattern Recognition and Computer Vision

With more powerful algorithms and greater computing power, the once "unreachable" pattern recognition and computer vision problems can now be resolved, simplifying complex decisions about input data. 274 In the last 20 years, interest in pattern recognition and computer vision problems has increased dramatically. This interest has in turn created a need for theoretical methods and experimental software and hardware to aid the design of computer vision and pattern recognition systems. Over 25 books have been published on these topics as have a number of conference proceedings and special issues of journals. * Pattern recognition machines and computer vision systems have been designed and built for everything from character recognition , target detection, medical diagnosis , analysis of biomedical signals and images, remote sensing, and identification of human faces and fingerprints , to reliability, socioeconomics, archaeology, speech recognition and understanding, machine part recognition , and automatic inspection. 1,2 In this article, we briefly review the most recent developments in pattern recognition and computer vision. Many definitions of pattern recognition have been proposed. We view pattern recognition here as being concerned primarily with the description and analysis of measurements taken from physical or mental processes. Pattern recognition often begins with some kind of preprocessing to remove noise and redundancy in the measurements , thereby ensuring an effective and efficient pattern description. Next, a set of characteristic measurements , numerical and/or nonnumeri-cal, and relations among these measurements are extracted to represent patterns. Patterns are then analyzed (classified and/or described) on the basis of the representation. Naturally, we need a good set of characteristic measurements and a firm idea of how they interrelate in representing patterns so that patterns can be easily recognized. Knowledge of the statistical and structural characteristics of patterns is vital to achieving this goal and should be fully utilized. From this point of view, then, pattern recognition means analyzing pattern characteristics as well as designing recognition systems.