Convex constrained meshes for superpixel segmentations of images

Jeremy Forsythe; Vitaliy Kurlin

doi:10.1117/1.JEI.26.6.061609

27 September 2017 Convex constrained meshes for superpixel segmentations of images

Jeremy Forsythe, Vitaliy Kurlin

Author Affiliations +

Funded by: Austrian Science Fund (FWF)

Journal of Electronic Imaging, Vol. 26, Issue 6, 061609 (September 2017). https://doi.org/10.1117/1.JEI.26.6.061609

Abstract

We consider the problem of splitting a pixel-based image into convex polygons with vertices at a subpixel resolution. The edges of the resulting polygonal superpixels can have any direction and should adhere well to object boundaries. We introduce a convex constrained mesh that accepts any straight line segments and outputs a complete mesh of convex polygons without small angles and with approximation guarantees for the given lines. Experiments on the Berkeley segmentation dataset BSD500 show that the resulting meshes of polygonal superpixels outperform other polygonal meshes on boundary recall and pixel-based simple linear iterative clustering and superpixels extracted via energy-driven sampling superpixels on undersegmentation errors.

Citation Download Citation

Jeremy Forsythe and Vitaliy Kurlin "Convex constrained meshes for superpixel segmentations of images," Journal of Electronic Imaging 26(6), 061609 (27 September 2017). https://doi.org/10.1117/1.JEI.26.6.061609

Received: 22 April 2017; Accepted: 1 September 2017; Published: 27 September 2017

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