Superpixels and Polygons using Simple Non-Iterative Clustering

We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal partitioning are superior to the respective state-of-the-art algorithms on quantitative benchmarks.


Publié dans:
30Th Ieee Conference On Computer Vision And Pattern Recognition (Cvpr 2017), 4895-4904
Présenté à:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, Hawaï, USA, July 21-26, 2017
Année
2017
Publisher:
New York, Ieee
ISSN:
1063-6919
ISBN:
978-1-5386-0457-1
Mots-clefs:
Laboratoires:




 Notice créée le 2017-04-06, modifiée le 2019-12-05

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