Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
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
Genetic programming for image analysis
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Genetic programming for cross-task knowledge sharing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Knowledge reuse in genetic programming applied to visual learning
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generative learning of visual concepts using multiobjective genetic programming
Pattern Recognition Letters
Multitask visual learning using genetic programming
Evolutionary Computation
Texture segmentation by genetic programming
Evolutionary Computation
Learning and Recognition of Hand-Drawn Shapes Using Generative Genetic Programming
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Learning Motion Detectors by Genetic Programming
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Interest point detection through multiobjective genetic programming
Applied Soft Computing
The unconstrained automated generation of cell image features for medical diagnosis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Networks of transform-based evolvable features for object recognition
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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A Genetic Programming (GP) method uses multiple runs, data decomposition stages, to evolve a hierarchical set of vehicle detectors for the automated inspection of infrared line scan imagery that has been obtained by a low flying aircraft. The performance on the scheme using two different sets of GP terminals (all are rotationally invariant statistics of pixel data) is compared on 10 images. The discrete Fourier transform set is found to be marginally superior to the simpler statistics set that includes an edge detector. An analysis of detector formulae provides insight on vehicle detection principles. In addition, a promising family of algorithms that take advantage of the GP method's ability to prescribe an advantageous solution architecture is developed as a post-processor. These algorithms selectively reduce false alarms by exploring context, and determine the amount of contextual information that is required for this task.