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Geodesic Active Contours (1997)

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by Vicent Caselles , Ron Kimmel , Guillermo Sapiro
Citations:1425 - 47 self
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BibTeX

@MISC{Caselles97geodesicactive,
    author = {Vicent Caselles and Ron Kimmel and Guillermo Sapiro},
    title = { Geodesic Active Contours},
    year = {1997}
}

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Abstract

A novel scheme for the detection of object boundaries is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve lays in a Riemannian space whose metric is defined by the image content. This geodesic approach for object segmentation allows to connect classical “snakes ” based on energy minimization and geometric active contours based on the theory of curve evolution. Previous models of geometric active contours are improved, allowing stable boundary detection when their gradients suffer from large variations, including gaps. Formal results concerning existence, uniqueness, stability, and correctness of the evolution are presented as well. The scheme was implemented using an efficient algorithm for curve evolution. Experimental results of applying the scheme to real images including objects with holes and medical data imagery demonstrate its power. The results may be extended to 3D object segmentation as well.

Keyphrases

geodesic active contour    kluwer academic publisher    minimal distance curve    curve evolution    active contour    geometric active contour    object segmentation    real image    image content    intrinsic geometric measure    several object    efficient algorithm    geodesic approach    energy minimization    medical data imagery demonstrate    stable boundary detection    object boundary    novel scheme    previous model    exterior boundary    simultaneous detection    formal result    classical snake    experimental result    riemannian space    large variation   

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