Resolution-independent superpixels based on convex constrained meshes without small angles

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Real-Time Shape Acquisition with Sensor-Specific Precision
• Date: December 2016
• Journal: Lecture Notes in Computer Science (LNCS)
• Volume: 10072
• Location: Las Vegas
• Lecturer: Jeremy Forsythe
• ISSN: 0302-9743
• Event: ISVC 2016
• Conference date: 12. December 2016 – 14. December 2016
• Pages: 223 – 233
• Keywords: superpixels, polygonal mesh, Delaunay triangulation, constrained triangulation, edge detection

Abstract

The over-segmentation problem for images is studied in the new resolution-independent formulation when a large image is approximated by a small number of convex polygons with straight edges at subpixel precision. These polygonal superpixels are obtained by refining and extending subpixel edge segments to a full mesh of convex polygons without small angles and with approximation guarantees. Another novelty is the objective error difference between an original pixel-based image and the reconstructed image with a best constant color over each superpixel, which does not need human segmentations. The experiments on images from the Berkeley Segmentation Database show that new meshes are smaller and provide better approximations than the state-of-the-art.

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BibTeX

@article{forsythe-2016-ccm,
  title =      "Resolution-independent superpixels based on convex
               constrained meshes without small angles",
  author =     "Jeremy Forsythe and Vitaliy Kurlin and Andrew Fitzgibbon",
  year =       "2016",
  abstract =   "The over-segmentation problem for images is studied in the
               new resolution-independent formulation when a large image is
               approximated by a small number of convex polygons with
               straight edges at subpixel precision. These polygonal
               superpixels are obtained by refining and extending subpixel
               edge segments to a full mesh of convex polygons without
               small angles and with approximation guarantees. Another
               novelty is the objective error difference between an
               original pixel-based image and the reconstructed image with
               a best constant color over each superpixel, which does not
               need human segmentations. The experiments on images from the
               Berkeley Segmentation Database show that new meshes are
               smaller and provide better approximations than the
               state-of-the-art.",
  month =      dec,
  journal =    "Lecture Notes in Computer Science (LNCS)",
  volume =     "10072",
  issn =       "0302-9743",
  pages =      "223--233",
  keywords =   "superpixels, polygonal mesh, Delaunay triangulation,
               constrained triangulation, edge detection",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2016/forsythe-2016-ccm/",
}