Skip to main content
SpringerLink
Log in

Boundary Detection

  • Reference work entry
  • First Online:
Computer Vision

  • 410 Accesses

Synonyms

Boundary extraction; Contour detection

Related Concepts

Edge Detection

Definition

Boundary detection is the process of detecting and localizing salient boundaries between objects in a scene.

Background

Boundary detection is closely related to, but not identical with, edge detection. Edge detection is a classical problem in computer vision which aims at finding brightness discontinuities. Edge detection is usually viewed as a low-level process of feature extraction that works under the assumption of ideal edge models (such as step and ridge edges).

In comparison, boundary detection is usually viewed as a mid-level process of finding boundaries of (and between) objects in scenes, thus having close ties with both grouping/segmentation and object shape. A large-scale dataset of natural images with human-marked groundtruth boundaries, the Berkeley Segmentation Dataset (BSDS) [1, 2], was established in 2001 and quickly became the standard benchmark for both boundary detection and...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 649.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 899.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Martin D, Fowlkes C, Malik J (2002) Berkeley segmentation dataset. http://www.cs.berkeley.edu/projects/vision/bsds

  2. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of international conference on computer vision (ICCV), Vancouver, vol 2, pp 416–423

    Google Scholar 

  3. Faugeras O (1993) Three-dimensional computer vision: a geometric viewpoint. MIT, Cambridge

    Google Scholar 

  4. Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc Lond B 207(1167):187–217

    Article  Google Scholar 

  5. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:679–698

    Article  Google Scholar 

  6. Morrone M, Owens R (1987) Feature detection from local energy. Pattern Recognit Lett 6:303–13

    Article  Google Scholar 

  7. Lindeberg T (1998) Edge detection and ridge detection with automatic scale selection. Int J Comput Vis 30:117–156

    Article  Google Scholar 

  8. Martin D, Fowlkes C, Malik, J (2004) Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5): 530–549

    Article  Google Scholar 

  9. Arbelaez P, Maire M, Fowlkes C, Malik J (2010) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916

    Article  Google Scholar 

  10. Dollar P, Tu Z, Belongie S (2006) Supervised learning of edges and object boundaries. In: Proceedings of computer vision and pattern recognition (CVPR), New York, vol 2, pp 1964–1971

    Google Scholar 

  11. Mumford D, Shah J (1989) Optimal approximation by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42:577685

    Article  MathSciNet  Google Scholar 

  12. Ren X, Fowlkes C, Malik J (2008) Learning probabilistic models for contour completion in natural images. Int J Comput Vis 77(1–3):47–64

    Article  Google Scholar 

  13. Zhu Q, Song G, Shi J (2007) Untangling cycles for contour grouping. In: Proceedings of international conference on computer vision (ICCV), Vancouver, pp 1–8

    Google Scholar 

  14. Malik J, Belongie S, Leung T, Shi J (2001) Contour and texture analysis for image segmentation. Int J Comput Vis 43(1):7–27

    Article  MATH  Google Scholar 

  15. Borenstein E, Ullman S (2002) Class-specific, top-down segmentation. In: Proceedings of european conference on computer vision (ECCV), Copenhagen, vol 2, pp 109–124

    Google Scholar 

  16. Leung T, Malik J (1998) Contour continuity in region-based image segmentation. In: Proceedings of european conference on computer vision (ECCV), Freiburg, Germany. vol 1, pp 544–559

    Google Scholar 

  17. Mori G, Ren X, Efros A, Malik J (2004) Recovering human body configurations: combining segmentation and recognition. In: Proceedings of computer vision and pattern recognition (CVPR), Washington, vol 2, pp 326–333

    Google Scholar 

  18. Hoiem D, Efros A, Hebert M (2007) Recovering occlusion boundaries from a single image. In: Proceedings of international conference on computer vision (ICCV), Rio de Janeiro

    Google Scholar 

  19. Berg A, Berg T, Malik J (2005) Shape matching and object recognition using low distortion correspondence. In: Proceedings of computer vision and pattern recognition (CVPR), San Diego, vol 1, pp 26–33

    Google Scholar 

  20. Ferrari V, Tuytelaars T, Gool LV (2006) Object detection by contour segment networks. In: Proceedings of european conference on computer vision (ECCV), Graz, pp 14–28

    Google Scholar 

  21. Gu C, Lim J, Arbeláez P, Malik J (2009) Recognition using regions. In: Proceedings of computer vision and pattern recognition (CVPR), Miami

    Google Scholar 

  22. Catanzaro B, Su BY, Sundaram N, Lee Y, Murphy M, Keutzer K (2009) Efficient, high-quality image contour detection. In: Proceedings of international conference on computer vision (ICCV), Kyoto, pp 2381–2388

    Google Scholar 

  23. Maire M, Arbelaez P, Fowlkes C, Malik J (2008) Using contours to detect and localize junctions in natural images. Proceedings of computer vision and pattern recognition (CVPR), Anchorage, pp 1–8

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intel Science and Technology Center for Pervasive Computing, Intel Labs, Seattle, WA, USA

    Xiaofeng Ren

Authors
  1. Xiaofeng Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Katsushi Ikeuchi

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Ren, X. (2014). Boundary Detection. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_215

Download citation

Publish with us

Policies and ethics

Search

Navigation