Special issue on real-time image and video processing for pattern recognition systems and applications (original) (raw)

The overarching goal of the pattern recognition community 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; nevertheless, it does not prioritize in memory and time complexity when matching models to observations. Given that we describe, explain and manipulate these objects through the perceptual system, there is an increasing need to favor those pattern recognition techniques that can explain, process and predict large volumes of visual data in realtime. 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 are conflicting objectives. To make pattern recognition techniques viable for practical applications (such as surveillance, robotics and medical applications), considerations such as computational complexity reduction, hardware implementation, software optimization, and strategies for parallelizing solutions must be observed.