Conference Paper

Robust Active Contour Segmentation with an Efficient Global Optimizer.

DOI: 10.1007/978-3-642-23687-7_18 Conference: Advances Concepts for Intelligent Vision Systems - 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings
Source: DBLP


Active contours or snakes are widely used for segmentation and tracking. Recently a new active contour model was proposed,
combining edge and region information. The method has a convex energy function, thus becoming invariant to the initialization
of the active contour. This method is promising, but has no regularization term. Therefore segmentation results of this method
are highly dependent of the quality of the images. We propose a new active contour model which also uses region and edge information,
but which has an extra regularization term. This work provides an efficient optimization scheme based on Split Bregman for
the proposed active contour method. It is experimentally shown that the proposed method has significant better results in
the presence of noise and clutter.

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    ABSTRACT: We use an unconditionally stable numerical scheme to im- plement a fast version of the geodesic active contour model. The proposed scheme is useful for object segmentation in images, like tracking moving objects in a sequence of images. The method is based on the Weickert- Romeney-Viergever [33] AOS scheme. It is applied at small regions, mo- tivated by Adalsteinsson-Sethian [1] level set narrow band approach, and uses Sethian’s fast marching method [26] for re-initialization. Experimen- tal results demonstrate the power of the new method for tracking in color movies.
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    ABSTRACT: We introduce a new class of active contour models that hold great promise for region and shape modelling, and we apply a special case of these models to the extraction of road networks from satellite and aerial imagery. The new models are arbitrary polynomial functionals on the space of boundaries, and thus greatly generalize the linear functionals used in classical contour energies. While classical energies are expressed as single integrals over the contour, the new energies incorporate multiple integrals, and thus describe long-range interactions between different sets of contour points. As prior terms, they describe families of contours that share complex geometric properties, without making reference to any particular shape, and they require no pose estimation. As likelihood terms, they can describe multi-point interactions between the contour and the data. To optimize the energies, we use a level set approach. The forces derived from the new energies are non-local however, thus necessitating an extension of standard level set methods. Networks are a shape family of great importance in a number of applications, including remote sensing imagery. To model them, we make a particular choice of prior quadratic energy that describes reticulated structures, and augment it with a likelihood term that couples the data at pairs of contour points to their joint geometry. Promising experimental results are shown on real images.
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