TurboPixels: Fast Superpixels Using Geometric Flows

University of Toronto · McGill University · +1 more institution

PubMed
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Abstract

We describe a geometric-flow-based algorithm for computing a dense oversegmentation of an image, often referred to as superpixels. It produces segments that, on one hand, respect local image boundaries, while, on the other hand, limiting undersegmentation through a compactness constraint. It is very fast, with complexity that is approximately linear in image size, and can be applied to megapixel sized images with high superpixel densities in a matter of minutes. We show qualitative demonstrations of high-quality results on several complex images. The Berkeley database is used to quantitatively compare its performance to a number of oversegmentation algorithms, showing that it yields less undersegmentation than…

Citation impact

1,140
total citations
FWCI
32.83
Percentile
100%
References
31
Citations per year

Authors

6

Topics & keywords

Keywords
  • Compact space
  • Constraint (computer-aided design)
  • Image (mathematics)
  • Limiting
  • Speedup
  • Artificial intelligence
  • Computer science
  • Computer vision
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