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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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Cites

Research works printed but not linked: 38

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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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