Cage Active Contours for image warping and morphing View Full Text


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Article Info

DATE

2018-12

AUTHORS

Jeroni Carandell, Luis Garrido, Laura Igual

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. More... »

PAGES

10

References to SciGraph publications

  • 2002-02. Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1999-11. Curvature scale space image in shape similarity retrieval in MULTIMEDIA SYSTEMS
  • 1988-01. Snakes: Active contour models in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1997-02. Geodesic Active Contours in INTERNATIONAL JOURNAL OF COMPUTER VISION
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    http://scigraph.springernature.com/pub.10.1186/s13640-018-0248-z

    DOI

    http://dx.doi.org/10.1186/s13640-018-0248-z

    DIMENSIONS

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