| Designing object detection and recognition systems that work in the real world is a challenging task due to various factors including the high complexity of the systems, the dynamically changing environment of the real world and factors such as occlusion, clutter, articulation, and various noise contributions that make the extraction of reliable features quite difficult. Furthermore, features useful to the detection and recognition of one kind of object or in the processing of one kind of imagery may not be effective in the detection and recognition of another kind of object or in the processing of another kind of imagery. Thus, the detection and recognition system often needs thorough overhaul when applied to other types of images different from the one for which the system was designed. This is very uneconomical and requires highly trained experts. The purpose of incorporating learning into the system design is to avoid the time consuming process of feature generation and selection and to lower the cost of building object detection and recognition systems.
Evolutionary computation is becoming increasingly important for computer vision and pattern recognition fields. It provides a systematic way of synthesis and analysis of object detection and recognition systems. With learning incorporated, the resulting recognition systems will be able to automatically generate new features on the fly and cleverly select a good subset of features according to the type of objects and images to which they are applied. The system will be flexible and can be applied to a variety of objects and images.
This book investigates evolutionary computational techniques such as genetic programming (GP), linear genetic programming (LGP), coevolutionary genetic programming (CGP) and genetic algorithms (GA) to automate the synthesis and analysis of object detection and recognition systems. The ultimate goal of the learning approaches presented in this book is to lower the cost of designing object detection and recognition systems and build more robust and flexible systems with human-competitive performance. |