Edge Detector Evolution using Multidimensional Multiobjective Genetic Programming

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@Misc{10.1.1.99.3617,

• author = "Yang Zhang and Peter I. Rockett",
• title = "Edge Detector Evolution using Multidimensional Multiobjective Genetic Programming",
• howpublished = "citeseerx",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.99.3617&rep=rep1&type=pdf",
• size = "25 pages",
• abstract = "In this paper we report the evolution of a feature extraction stage for edge detection using multidimensional multiobjective genetic programming. We have employed training and validation data produced using a realistic model of the imaging physics to evolve an n2-to-m mapping which projects the pixel intensities of an n by n image patch into an m-dimensional decision space. The (near-)optimal value of m is also simultaneously determined during evolution. A conventional Fisher linear discriminant is then used to classify edge patterns. On the independent validation set, the suggested edge detector is shown to give performance superior to both the well-known conventional Canny detector and to earlier multi-objective genetic programming results which projected the pattern vector into a one-dimensional decision space. In addition, the superiority of the new detector is also demonstrated on a hand-labeled set of real images.",
• notes = "See \cite{Zhang:2009:EC}",

}

Genetic Programming entries for Yang Zhang Peter I Rockett

Citations