Abstract
Conventional edge detectors are not very useful for generating an edge map to be used in the search of a concrete object with deformable models or genetic algorithms. In this work, a selective color edge detector is presented, which is able to obtain the edges in the image and determine whether or not those edges are originated in a concrete object. The system is based on a multilayer perceptron neural network, which classifies the edges previously detected by the multidimensional gradient (color images), and is trained using some images of the searched object whose edges are known. The method has been successfully applied to bovine livestock images, obtaining edge maps to be used for a boundary extraction with genetic algorithms technique.
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Velasco, H.M.G., Orellana, C.J.G., Macías, M.M., Caballero, R.G. (2005). Selective Color Edge Detector Based on a Neural Classifier. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_11
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DOI: https://doi.org/10.1007/11558484_11
Publisher Name: Springer, Berlin, Heidelberg
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