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.
J. Forsythe—Supported by the Austrian Science Fund (FWF) project P24600-N23 at TU Wien.
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Forsythe, J., Kurlin, V., Fitzgibbon, A. (2016). Resolution-Independent Superpixels Based on Convex Constrained Meshes Without Small Angles. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_21
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DOI: https://doi.org/10.1007/978-3-319-50835-1_21
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