Introduction -- Vision and Evolution: The State of the Art -- Evolutionary Computing -- Accurate Modeling of Image Features Using Evolutionary Computing -- Evolutionary Synthesis of Interest Point Detectors Through Genetic Programming -- The Honeybee Search Algorithm -- Multiobjective Sensor Planning for Accurate Reconstruction -- Evolutionary Visual Learning with Linear Genetic Programming -- Evolutionary Synthesis of Feature Descriptor Operators with Genetic Programming -- Summary and Conclusions -- App. A, Camera Calibration and Stereoscopic Vision -- Glossary -- Index.

This book explains the theory and application of evolutionary computer vision, a new paradigm where challenging vision problems can be approached using the techniques of evolutionary computing. This methodology achieves excellent results for defining fitness functions and representations for problems by merging evolutionary computation with mathematical optimization to produce automatic creation of emerging visual behaviors. In the first part of the book the author surveys the literature in concise form, defines the relevant terminology, and offers historical and philosophical motivations for the key research problems in the field. For researchers from the computer vision community, he offers a simple introduction to the evolutionary computing paradigm. The second part of the book focuses on implementing evolutionary algorithms that solve given problems using working programs in the major fields of low-, intermediate- and high-level computer vision. This book will be of value to researchers, engineers, and students in the fields of computer vision, evolutionary computing, robotics, biologically inspired mechatronics, electronics engineering, control, and artificial intelligence.