Contour and texture analysis for image segmentation

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Standard

Contour and texture analysis for image segmentation. / Malik, Jitendra; Belongie, Serge; Leung, Thomas; Shi, Jianbo.

I: International Journal of Computer Vision, Bind 43, Nr. 1, 06.2001, s. 7-27.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Malik, J, Belongie, S, Leung, T & Shi, J 2001, 'Contour and texture analysis for image segmentation', International Journal of Computer Vision, bind 43, nr. 1, s. 7-27. https://doi.org/10.1023/A:1011174803800

APA

Malik, J., Belongie, S., Leung, T., & Shi, J. (2001). Contour and texture analysis for image segmentation. International Journal of Computer Vision, 43(1), 7-27. https://doi.org/10.1023/A:1011174803800

Vancouver

Malik J, Belongie S, Leung T, Shi J. Contour and texture analysis for image segmentation. International Journal of Computer Vision. 2001 jun.;43(1):7-27. https://doi.org/10.1023/A:1011174803800

Author

Malik, Jitendra ; Belongie, Serge ; Leung, Thomas ; Shi, Jianbo. / Contour and texture analysis for image segmentation. I: International Journal of Computer Vision. 2001 ; Bind 43, Nr. 1. s. 7-27.

Bibtex

@article{fba61c1d0ae4491db9a02b77e97e31da,
title = "Contour and texture analysis for image segmentation",
abstract = "This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.",
keywords = "Cue integration, Grouping, Normalized cut, Segmentation, Texton, Texture",
author = "Jitendra Malik and Serge Belongie and Thomas Leung and Jianbo Shi",
note = "Funding Information: The authors would like to thank the Berkeley vision group, especially Chad Carson, Alyosha Efros, David Forsyth, and Yair Weiss for useful discussions during the development of the algorithm. We thank Doron Tal for implementing the algorithm in C++. This research was supported by (ARO) DAAH04-96-1-0341, the Digital Library Grant IRI-9411334, NSF Graduate Fellowships to SB and JS and a Berkeley Fellowship to TL.",
year = "2001",
month = jun,
doi = "10.1023/A:1011174803800",
language = "English",
volume = "43",
pages = "7--27",
journal = "International Journal of Computer Vision",
issn = "0920-5691",
publisher = "Springer",
number = "1",

}

RIS

TY - JOUR

T1 - Contour and texture analysis for image segmentation

AU - Malik, Jitendra

AU - Belongie, Serge

AU - Leung, Thomas

AU - Shi, Jianbo

N1 - Funding Information: The authors would like to thank the Berkeley vision group, especially Chad Carson, Alyosha Efros, David Forsyth, and Yair Weiss for useful discussions during the development of the algorithm. We thank Doron Tal for implementing the algorithm in C++. This research was supported by (ARO) DAAH04-96-1-0341, the Digital Library Grant IRI-9411334, NSF Graduate Fellowships to SB and JS and a Berkeley Fellowship to TL.

PY - 2001/6

Y1 - 2001/6

N2 - This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.

AB - This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.

KW - Cue integration

KW - Grouping

KW - Normalized cut

KW - Segmentation

KW - Texton

KW - Texture

UR - http://www.scopus.com/inward/record.url?scp=0035358181&partnerID=8YFLogxK

U2 - 10.1023/A:1011174803800

DO - 10.1023/A:1011174803800

M3 - Journal article

AN - SCOPUS:0035358181

VL - 43

SP - 7

EP - 27

JO - International Journal of Computer Vision

JF - International Journal of Computer Vision

SN - 0920-5691

IS - 1

ER -

ID: 302058963