Evolving Object Detectors with a GPU Accelerated Vision System

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Ebner:2010b,

• author = "Marc Ebner",
• title = "Evolving Object Detectors with a GPU Accelerated Vision System",
• booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010",
• year = "2010",
• editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller",
• series = "Lecture Notes in Computer Science",
• volume = "6274",
• pages = "109--120",
• address = "York",
• month = sep # " 6-8",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, GPU",
• isbn13 = "978-3-642-15322-8",
• DOI = "doi:10.1007/978-3-642-15323-5_10",
• abstract = "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.",
• affiliation = "Wilhelm-Schickard-Institut fur Informatik, Eberhard-Karls-Universitat Tuebingen, Abt. Rechnerarchitektur, Sand 1, 72076 Tbingen, Germany",

}

Genetic Programming entries for Marc Ebner

Citations