Contour Detection and Hierarchical Image Segmentation

University of California, Berkeley · California Institute of Technology · +1 more institution

PubMed
Indexed incrossrefpubmed

Abstract

This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical…

Citation impact

5,524
total citations
FWCI
132.67
Percentile
100%
References
83
Citations per year

Authors

4

Topics & keywords

Keywords
  • Computer science
  • Segmentation
  • Artificial intelligence
  • Scale-space segmentation
  • Image segmentation
  • Segmentation-based object categorization
  • Computer vision
  • Cluster analysis
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