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Cited by 5,454

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Contour Detection and Hierarchical Image Segmentation

Arbeláez, P, Maire, M, Fowlkes, C, Malik, J·2011·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2010.161
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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 segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

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Cited by

Metadata

Authors
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2011-05
Volume
33
Issue
5
Pages
898-916
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
DOI
10.1109/tpami.2010.161
OpenAlex
W2110158442
Cited by
5,454
References
5
FWCI
151.11
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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