Abstract
In this paper we present a new classification and image segmentation system based on the addition of a variational method to a classic clustering algorithm. This system constitutes an improvement respect traditional segmentation methods. Often due to the nature of the texture features obtained from an image, the segmentation results are not quite precise. If this happens, using the energy functional and its minimization can improve the segmentation. This functional takes into account the information in the feature space and the information in the 2D image domain. The extracted characteristics from the image are texture features that have been defined in order to obtain an admissible trade-off between their discriminant capacity and their effectiveness to be implemented in a vision board to operate at real time. We show some results to appreciate this improvement in the segmentation using the energy functional.
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© 1997 Springer-Verlag Berlin Heidelberg
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Grau, A., Saludes, J. (1997). Improved textured images segmentation using an energy functional. In: Del Bimbo, A. (eds) Image Analysis and Processing. ICIAP 1997. Lecture Notes in Computer Science, vol 1310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63507-6_186
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DOI: https://doi.org/10.1007/3-540-63507-6_186
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