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• W2803608481 abstract "We propose and numerically solve a new variational model for automatic saliency detection in digital images. Using a non-local framework we consider a family of edge preserving functions combined with a new quadratic saliency detection term. Such term defines a constrained bilateral obstacle problem for image classification driven by p-Laplacian operators, including the so-called hyper-Laplacian case (0 < p < 1). The related non-convex non-local reactive flows are then considered and applied for glioblastoma segmentation in magnetic resonance fluid-attenuated inversion recovery (MRI-Flair) images. A fast convolutional kernel based approximated solution is computed. The numerical experiments show how the non-convexity related to the hyperLaplacian operators provides monotonically better results in terms of the standard metrics." @default.
• W2803608481 created "2018-06-01" @default.
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• W2803608481 date "2018-05-23" @default.
• W2803608481 modified "2023-09-27" @default.
• W2803608481 title "Non-convex non-local flows for saliency detection." @default.
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• W2803608481 hasPublicationYear "2018" @default.
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