Special Issue on Real-Time Image and Video Processing for Pattern Recognition Systems and Applications

摘要

The overarching approach in pattern recognition consists of presenting hypotheses to describe classes of objects using mathematical models, processing the information to eliminate the presence of noise, and selecting the model that best explains the given observations; it does not consider memory and time complexity constraints when matching models to the observations. Given that we describe, explain and manipulate these objects through visual sensing, there is an increasing need to favor those pattern recognition techniques that can explain large volumes of visual data in real-time. Such techniques cannot be developed "in-vitro" due to the physical constraints of the complex environment and the context in which these techniques are used. Further, these new methods need to achieve high detection, classification and recognition accuracies in real-time even when these objectives conflict with each other. To make pattern recognition techniques viable for practical applications (such as medical and defense), considerations such as computational complexity reduction, hardware implementation, software optimization, and strategies for parallelizing solutions must be observed.