IEEE Trans Image Process - Harmonic active contours.

Tópicos

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{ imag(2675) segment(2577) method(1081) }
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Resumo

We propose a segmentation method based on the geometric representation of images as 2-D manifolds embedded in a higher dimensional space. The segmentation is formulated as a minimization problem, where the contours are described by a level set function and the objective functional corresponds to the surface of the image manifold. In this geometric framework, both data-fidelity and regularity terms of the segmentation are represented by a single functional that intrinsically aligns the gradients of the level set function with the gradients of the image and results in a segmentation criterion that exploits the directional information of image gradients to overcome image inhomogeneities and fragmented contours. The proposed formulation combines this robust alignment of gradients with attractive properties of previous methods developed in the same geometric framework: 1) the natural coupling of image channels proposed for anisotropic diffusion and 2) the ability of subjective surfaces to detect weak edges and close fragmented boundaries. The potential of such a geometric approach lies in the general definition of Riemannian manifolds, which naturally generalizes existing segmentation methods (the geodesic active contours, the active contours without edges, and the robust edge integrator) to higher dimensional spaces, non-flat images, and feature spaces. Our experiments show that the proposed technique improves the segmentation of multi-channel images, images subject to inhomogeneities, and images characterized by geometric structures like ridges or valleys.

Resumo Limpo

propos segment method base geometr represent imag d manifold embed higher dimension space segment formul minim problem contour describ level set function object function correspond surfac imag manifold geometr framework datafidel regular term segment repres singl function intrins align gradient level set function gradient imag result segment criterion exploit direct inform imag gradient overcom imag inhomogen fragment contour propos formul combin robust align gradient attract properti previous method develop geometr framework natur coupl imag channel propos anisotrop diffus abil subject surfac detect weak edg close fragment boundari potenti geometr approach lie general definit riemannian manifold natur general exist segment method geodes activ contour activ contour without edg robust edg integr higher dimension space nonflat imag featur space experi show propos techniqu improv segment multichannel imag imag subject inhomogen imag character geometr structur like ridg valley

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