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- W2128133096 abstract "A novel level set multiphase image segmentation method combined with kernel mapping is presented. A kernel function maps implicitly the original data into data of a higher dimension so that the piecewise constant model becomes applicable. The goal is to consider several types of noise by a single model. Gradient flow equations are iteratively derived in order to minimize the segmentation functional with respect to the partition, in a first step, and the regions parameters in a second step. Using a common kernel function, we verified the effectiveness of the method by a quantitative and comparative performance evaluation over experiments on synthetic images, as well as a variety of real images such as medical, SAR, and natural images." @default.
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- W2128133096 date "2009-11-01" @default.
- W2128133096 modified "2023-09-26" @default.
- W2128133096 title "Image segmentation in a kernel-induced space" @default.
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- W2128133096 doi "https://doi.org/10.1109/icip.2009.5414593" @default.
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