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Continually growing complexity of problems arising in digital imaging and necessity to deal with changing, dynamic, and uncertain information put higher demands on the existing systems of processing visual information. Their limitations and frequent inefficiency make researchers turn their attention to new methods capable of autonomous adjustment and selfadaptation to volatile inputs, tasks and conditions. This book investigates various aspects of using Evolutionary algorithms as a unified platform applied to a number of problems arising in image processing. In particular, the areas of image registration, and object or pattern recognition are addressed when the images of interest display significant distortion due to some physical or geometrical conditions. The book explores a broad range of models, algorithms, and techniques which implement advanced concepts of hybridization in Evolutionary algorithms and can improve their computational performance in solving imaging problems. The book will be useful for professionals working in the areas of digital image processing and Evolutionary computations.
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