Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Recombination, selection, and the genetic construction of computer programs
Recombination, selection, and the genetic construction of computer programs
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Target detection in SAR imagery by genetic programming
Advances in Engineering Software
Digital Image Processing
Evolution of Vehicle Detectors for Infrared Line Scan Imagery
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Learning Composite Operators For Object Detection
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic Programming for Automatic Target Classification and Recognition
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Genetic Programming for Feature Detection and Image Segmentation
Selected Papers from AISB Workshop on Evolutionary Computing
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In this paper, genetic programming (GP) with smart crossover and smart mutation is proposed to discover integrated feature agents that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in remotely sensed images. The motivation for using genetic programming is to overcome the limitations of human experts, since GP attempts many unconventional ways of combination, in some cases, these unconventional combinations yield exceptionally good results. Smart crossover and smart mutation identify and keep the effective components of integrated operators called "agents" and significantly improve the efficiency of GP. Our experimental results show that compared to normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly during training to effectively extract the regions-of-interest and the learned agents can be applied to extract ROIs in other similar images.