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Cited by 2,420

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Learning to detect natural image boundaries using local brightness, color, and texture cues

Martin, D.R., Fowlkes, C.C., Malik, J.·2004·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2004.1273918
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Abstract

The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images.

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Research works printed but not linked: 38

  1. 10.1109/ICCV.2001.937614 · DOI
  2. 10.1023/A:1011174803800 · DOI · Paper page
  3. 10.1109/ICCV.1995.466910 · DOI
  4. 10.1007/3-540-47969-4_21 · DOI
  5. Rubner, 1996, Coalescing Texture Descriptors, Proc. ARPA Image Understanding Workshop
  6. 10.1109/ICIP.2000.899596 · DOI
  7. 10.1109/TPAMI.2002.1023800 · DOI · Paper page
  8. 10.1007/3-540-47977-5_38 · DOI
  9. 10.1016/0042-6989(95)00056-6 · DOI
  10. 10.1109/ICCV.2001.937655 · DOI · Paper page
  11. 10.1109/CVPR.1999.786996 · DOI
  12. 10.1098/rspb.1988.0073 · DOI · Paper page
  13. 10.1109/ICCV.1990.139492 · DOI
  14. 10.1117/12.143648 · DOI · Paper page
  15. 10.1109/CVPR.1999.784624 · DOI
  16. 10.1109/ICCV.1999.790384 · DOI
  17. 10.1007/BF00204594 · DOI · Paper page
  18. 10.1364/JOSAA.7.000923 · DOI · Paper page
  19. 10.1109/ICIP.1995.537718 · DOI
  20. 10.1109/CVPR.1997.609331 · DOI · Paper page
  21. 10.1109/ICCV.1999.790412 · DOI · Paper page
  22. 10.1214/aoms/1177692631 · DOI
  23. 10.1109/ICCV.2001.937632 · DOI · Paper page
  24. Palmer, 1999, Vision Science
  25. 10.1007/978-981-96-1710-4 · DOI
  26. 10.1109/PROC.1979.11325 · DOI · Paper page
  27. 10.1109/CVPR.1999.786963 · DOI
  28. Martin, 2002, Learning to Detect Natural Image Boundaries Using Brightness and Texture, Neural Information Processing Systems
  29. Meila, 2001, Learning Segmentation by Random Walks, Neural Information Processing Systems
  30. 10.1023/A:1007614523901 · DOI · Paper page
  31. 10.1162/neco.1994.6.2.181 · DOI · Paper page
  32. Förstner, 1987, A Fast Operator for Detection and Precise Locations of Distinct Points, Corners, and Centres of Circular Features, Proc. Intercommission Conf. Fast Processing of Photogrammetric Data, 281
  33. 10.1007/3-540-56484-5 · DOI
  34. Martin, 2003, Local Boundary Detection in Natural Images: Matching Human and Machine Performance, Proc. European Conf. Visual Perception
  35. 10.1007/BF02278710 · DOI · Paper page
  36. 10.1007/BF02288323 · DOI
  37. 10.1007/bf01585996 · DOI
  38. 10.1007/3-540-59408-6_49 · DOI

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Metadata

Authors
Martin, D. R., Fowlkes, C. C., & Malik, J.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2004-05
Volume
26
Issue
5
Pages
530-549
ISSN
0162-8828 (ISSN-L)
DOI
10.1109/tpami.2004.1273918
OpenAlex
W2119823327
Cited by
2,420
References
3
FWCI
65.21
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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CoChat. (2026). Citation check: Learning to detect natural image boundaries using local brightness, color, and texture cues. https://app.cochat.ai/papers/10.1109/tpami.2004.1273918
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Martin, D. R., Fowlkes, C. C., & Malik, J. (2004). Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2004.1273918

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.