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Detection of Edges in Color Images: A Review and Evaluative Comparison of State-of-the-Art Techniques

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Autonomous and Intelligent Systems (AIS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7326))

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Abstract

We present an evaluative review of various edge detection techniques for color images that have been proposed in the last two decades. The statistics shows that color images contain 10% additional edge information as compared to their gray scale counterparts. This additional information is crucial for certain computer vision tasks. Although, several reviews of the work on gray scale edge detection are available, color edge detection has few. The latest review on color edge detection is presented by Koschan and Abidi in 2005. Much advancement in color edge detection has been made since then, and thus, a thorough review of state-of-art color edge techniques is much needed. The paper makes a review and evaluation of various color edge detection techniques to quantify their accuracy and robustness against noise. It is found that Minimum Vector Dispersion (MVD) edge detector has the best edge detection accuracy and Robust Color Morphological Gradient-Median-Mean (RCMG-MM) edge detector has highest robustness against the noise.

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Author information

Authors and Affiliations

  1. Department of Comp. Science & Engg., PEC University of Technology, Chandigarh, India

    Ajay Mittal & Sanjeev Sofat

  2. Department of Computer Science, University of York, York, UK

    Edwin Hancock

Authors
  1. Ajay Mittal
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  2. Sanjeev Sofat
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  3. Edwin Hancock
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel  & Fakhri Karray  & 

  2. Computation Intelligence Centre, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Hani Hagras

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© 2012 Springer-Verlag Berlin Heidelberg

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Mittal, A., Sofat, S., Hancock, E. (2012). Detection of Edges in Color Images: A Review and Evaluative Comparison of State-of-the-Art Techniques. In: Kamel, M., Karray, F., Hagras, H. (eds) Autonomous and Intelligent Systems. AIS 2012. Lecture Notes in Computer Science(), vol 7326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31368-4_30

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  • DOI: https://doi.org/10.1007/978-3-642-31368-4_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31367-7

  • Online ISBN: 978-3-642-31368-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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