Evolutionary Synthesis of Pattern Recognition Systems [Hardcover]

Integrates computer vision, pattern recognition, and AI.

Presents original research that will benefit researchers and professionals in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology

 

Evolutionary computation is becoming increasingly important for computer vision and pattern recognition and provides a systematic way of synthesis and analysis of object detection and recognition systems. Incorporating learning into recognition systems will enable these systems 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.

This unique monograph investigates evolutionary computational techniques---such as genetic programming, linear genetic programming, coevolutionary genetic programming and genetic algorithms---to automate the synthesis and analysis of object detection and recognition systems. The book achieves four aims: 

*Shows the efficacy of genetic programming and coevolutionary genetic programming in synthesizing effective composite operators and composite features from domain-independent primitive image processing operations and primitive features (both elementary and complex) for object detection and recognition.

*Integrates smart crossover, smart mutation and a new fitness function based on minimum description length (MDL) principle in a design to improve genetic programming's efficiency

*Proposes a new MDL-based fitness function to improve the genetic algorithms performance on feature selection for object detection and recognition.

*Synthesizes recognition systems by using adaptive coevolutionary linear gl#1