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Edge Detection

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Computer Vision

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Related Concepts

Boun-dary Detection; Scale Selection

Definition

Edge detection is the process of label the image pixels that lie on the boundaries where abrupt intensity discontinuity occur.

Background

The light projected from a visual scene into an eye or camera is typically piecewise smooth as a function of visual angle. Since nearby points on a surface tend to have similar attitude, reflectance, and illumination, the pixels to which these surface points project tend to have similar intensity. This rule is broken when two adjacent pixels project from points on either side of an occlusion boundary, since the points now project from different surfaces that may well have different attitude, reflectance, and illumination, and typically an abrupt change in image intensity results. Intensity edges also arise when neighboring pixels project from points on the same surface that happens to straddle a surface crease, pigment change, or shadow boundary.

Since these abrupt changes in image...

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References

  1. Bergholm F (1987) Edge focusing. IEEE Trans Pattern Anal Mach Intell 9(6):726–741

    Article  Google Scholar 

  2. Canny J (1986) A computational approach to edge-detection. IEEE Trans Pattern Anal Mach Intell 8(6): 679–698

    Article  Google Scholar 

  3. Deriche R (1987) Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int J Comput Vis 1(2):167–187

    Article  Google Scholar 

  4. Elder JH (1999) Are edges incomplete? Int J Comput Vis 34(2–3):97–122

    Article  Google Scholar 

  5. Elder JH, Zucker SW (1996) Scale space localization, blur and contour-based image coding. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Los Alamitos, pp 27–34

    Google Scholar 

  6. Elder JH, Zucker SW (1998) Local scale control for edge detection and blur estimation. IEEE Trans Pattern Anal Mach Intell 20(7):699–716

    Article  Google Scholar 

  7. Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906

    Article  Google Scholar 

  8. Heath M, Sarkar S, Sanocki T, Bowyer K (1998) Comparison of edge detectors – a methodology and initial study. Comput Vis Image Underst 69(1):38–54

    Article  Google Scholar 

  9. Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195:215–243

    Article  Google Scholar 

  10. Hueckel MH (1971) An operator which locates edges in digitized pictures. J Assoc Comput Mach 18:113–125

    Article  MATH  Google Scholar 

  11. Iverson LA, Zucker SW (1995) Logical/linear operators for image curves. IEEE Trans Pattern Anal Mach Intell 17(10):982–996

    Article  Google Scholar 

  12. Kass M, Witkin A, Terzopoulos D (1987) Snakes – active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  13. Konishi S, Yuille AL, Coughlan JM, Zhu SC (2003) Statistical edge detection: learning and evaluating edge cues. IEEE Trans Pattern Anal Mach Intell 25(1):57–74

    Article  Google Scholar 

  14. Lee HC, Cok DR (1991) Detecting boundaries in a vector field. IEEE Trans Signal Process 39(5):1181–1194

    Article  Google Scholar 

  15. Lindeberg T (1998) Edge detection and ridge detection with automatic scale selection. Int J Comput Vis 30(2):117–154

    Article  Google Scholar 

  16. Mallat S, Zhong S (1992) Characterization of signals from multiscale edges. IEEE Trans Pattern Anal Mach Intell 14(7):710–732

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Martin DR, Fowlkes CC, 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 

  19. Perona P, Malik J (1990) Scale-space and edge-detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639

    Article  Google Scholar 

  20. Roberts L (1965) Machine perception of 3-dimensional solids. In: Tippett J (ed) Optical and electro-optical information processing. MIT, Cambridge, MA

    Google Scholar 

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Author information

Authors and Affiliations

  1. Centre for Vision Research, York University, Toronto, ON, Canada

    James H. Elde

Authors
  1. James H. Elde
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Corresponding author

Correspondence to James H. Elde .

Editor information

Editors and Affiliations

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

    Katsushi Ikeuchi

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© 2014 Springer Science+Business Media New York

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Elde, J.H. (2014). Edge Detection. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_217

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