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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 techniquessuch as genetic programming, linear genetic programming, coevolutionary genetic programming and genetic algorithmsto automate the synthesis and analysis of object detection and recognition systems.The purpose of incorporating learning into the system design is to avoid the timeconsuming process of feature generation and selection and to reduce the cost of building object detection and recognition systems.Researchers, professionals, engineers, and students working in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology will find this welldeveloped and organized volume an invaluable resource.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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