Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Machine vision
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Computer Vision
Generating Image Filters for Target Recognition by Genetic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving Task Specific Image Operator
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
Mediated Reality Using Computer Graphics Hardware for Computer Vision
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
OpenGL(R) Shading Language (2nd Edition)
OpenGL(R) Shading Language (2nd Edition)
OpenVIDIA: parallel GPU computer vision
Proceedings of the 13th annual ACM international conference on Multimedia
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Computer Vision: A Taxonomic Tutorial
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
A Real-Time Evolutionary Object Recognition System
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Evolving edge detectors with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Genetic programming for image analysis
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Towards automated learning of object detectors
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Visual Learning by Evolutionary and Coevolutionary Feature Synthesis
IEEE Transactions on Evolutionary Computation
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Using GPU processing, it is now possible to develop an evolutionary vision system working at interactive frame rates. Our system uses motion as an important cue to evolve detectors which are able to detect an object when this cue is not available. Object detectors consist of a series of high level operators which are applied to the input image. A matrix of low level point operators are used to recombine the output of the high level operators. With this contribution, we investigate, which image processing operators are most useful for object detection. It was found that the set of image processing operators could be considerably reduced without reducing recognition performance. Reducing the set of operators lead to an increase in speedup compared to a standard CPU implementation.