Edge detection in the presence of noise is a well-known problem. This paper examines an applications-motivated approach for solving the problem using novel techniques and presents a method developed by the authors that performs well on a large class of targets. ROC curves are used to compare this method with other well-known edge detection operators, with favorable results. A theoretical argument is presented that favors LMMSE filtering over median filtering in extremely noisy scenes. Simulated results of the research are presented.

Object identification and tracking applications of pattern recognition at video rates is a problem of wide interest, with previous attempts limited to very simple threshold or correlation (restricted window) methods. New high-speed algorithms together with fast digital hardware have produced a system for missile and aircraft identification and tracking that possesses a degree of ``intelligence'' not previously implemented in a real-time tracking system. Adaptive statistical clustering and projection-based classification algorithms are applied in real time to identify and track objects that change in appearance through complex and nonstationary background/foreground situations. Fast estimation and prediction algorithms combine linear and quadratic estimators to provide speed and sensitivity. Weights are determined to provide a measure of confidence in the data and resulting decisions. Strategies based on maximizing the probability of maintaining track are developed. This paper emphasizes the theoretical aspects of the system and discusses the techniques used to achieve real-time implementation.