An Overview of Advances of Pattern Recognition Systems in Computer Vision

Abstract

As mentioned before, pattern recognition does not appear as a new problem. A lot of studies have been performed on this scientific field and a lot of works are currently developed. Pattern recognition is a wide topic in machine learning. It aims to classify a pattern into one of a number of classes. It appears in various fields like psychology, agriculture, computer vision, robotics , biometrics... With technological improvements and growing performances of computer science, its application field has no real limitation. In this context, a challenge consists of finding some suitable description features since commonly, the pattern to be classified must be represented by a set of features characterising it. These features must have discriminative properties: efficient features must be affined transformations insensitive. They must be robust against noise and against elastic deformations due, e.g., to movement in pictures. Through the application example based on our MSGPR method, we have illustrated various aspects of a PRS. With this example, we have illustrated the description task that enabled us to extract multi-scale features from the generated IPM function. By using theses features in the classification task, we identified the letters from a car number plate so that we automatically retrieved the license number of a vehicle.