Abstract
Cage Active Contours (CACs) have shown to be a framework for segmenting connected objects using a new class of parametric region-based active contours. The CAC approach deforms the contour locally by moving cage’s points through affine transformations. The method has shown good performance for image segmentation, but other applications have not been studied. In this paper, we extend the method with new energy functions based on Gaussian mixture models to capture multiple color components per region and extend their applicability to RGB color space. In addition, we provide an extended mathematical formalization of the CAC framework with the purpose of showing its good properties for segmentation, warping, and morphing. Thus, we propose a multiple-step combined method for segmenting images, warping the correspondences of the object cage points, and morphing the objects to create new images. For validation, both quantitative and qualitative tests are used on different datasets. The results show that the new energies produce improvements over the previously developed energies for the CAC. Moreover, we provide examples of the application of the CAC in image segmentation, warping, and morphing supported by our theoretical conclusions.
Highlights
Cage Active Contours (CACs), proposed in [18], are a framework for segmenting connected objects using a new class of parametric region-based active contours
We present the multivariate Gaussian mixture energy function (MGM), which is expressed in the following way: EMixtGauss =
4 Results and discussion We show the experimental results obtained for the enhanced Gaussian energy function as well as for the shape similarity approach
Summary
Cage Active Contours (CACs), proposed in [18], are a framework for segmenting connected objects using a new class of parametric region-based active contours. The evolving contour is parametrized by an ordered set of control points, using mean value coordinates (a distinct generalization of barycentric coordinates), called a cage. The CAC approach deforms the contour locally by moving the cage’s points through affine transformations. The cage allows to introduce other restrictive criteria (e.g., avoid self-intersections), apart from the already intrinsic properties of the mean value coordinates such as smoothness [17]. The properties of the CAC method allow to deal with region-based models which proves to be hugely advantageous with respect to most previous parametrized approaches, which are only able to deal with edge-based energies. As far as we know, except for [7], which treats 3D images, there is almost no work in the
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