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A Morphological Edge Detector for Gray-Level Image Thresholding

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Image Analysis and Recognition (ICIAR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3656))

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Abstract

A morphological edge detector for robust real time image segmentation is proposed in this paper. Different from traditional thresholding methods that determine the threshold based on image gray level distribution, our method derives the threshold from object boundary point gray values and the boundary points are detected in the image using the proposed morphological edge detector. Firstly, the morphological edge detector is applied to compute the image morphological gradients. Then from the resultant image morphological gradient histogram, the object boundary points can be selected, which have higher gradient values than those of points within the object and background. The threshold is finally determined from the object boundary point gray values. Thus noise points inside the object and background are avoided in threshold computation. Experimental results on currency image segmentation for real time printing quality inspection are rather encouraging.

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Authors and Affiliations

  1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, Sichuan, 610041, P.R. China

    Bin Chen & Ping Liu

  2. Information Technology Department, Armstrong Atlantic State University, Savannah, Georgia, 31419, USA

    Lei He

Authors
  1. Bin Chen
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  2. Lei He
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  3. Ping Liu
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Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, P.O. Box, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2005 Springer-Verlag Berlin Heidelberg

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Chen, B., He, L., Liu, P. (2005). A Morphological Edge Detector for Gray-Level Image Thresholding. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_81

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  • DOI: https://doi.org/10.1007/11559573_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29069-8

  • Online ISBN: 978-3-540-31938-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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