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Common Vector Approach Based Image Gradients Computation for Edge Detection

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Mathematical Aspects of Computer and Information Sciences (MACIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11989))

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Abstract

In this study, the concept of Common Vector Approach (CVA) is adopted for image gradients computation in terms of revealing edge maps stated on images. Firstly, noise stated on image is smoothed by Gaussian filtering, secondly gradient map computation using CVA is carried out, then the angle and direction maps are obtained from the gradient map and lastly peak points are selected and a smart routing procedure is performed to linking them. With an unusual methodology, the derivatives of image through vertical and horizontal directions have obtained by utilizing the CVA, which is the crucial step and gained the novelty to this work. To compare results objectively, we have judged the performance with respect to a comparison metric called ROC Curve analysis. As a contribution to the edge detection area, CVA-ED presents satisfactory results and edge maps produced can be used in the tasks of object tracking, motion estimation and image retrieval.

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Authors and Affiliations

  1. Computer Engineering Department, Eskisehir Osmangazi University, 26480, Eskişehir, Turkey

    Sahin Isik & Kemal Ozkan

Authors
  1. Sahin Isik
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  2. Kemal Ozkan
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Correspondence to Sahin Isik .

Editor information

Editors and Affiliations

  1. AIT Austrian Institute of Technology, Vienna, Austria

    Daniel Slamanig

  2. IMJ-PRG, Sorbonne University, Paris, France

    Elias Tsigaridas

  3. Institute of Information Technologies, Gebze Technical University, Gebze, Turkey

    Zafeirakis Zafeirakopoulos

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Isik, S., Ozkan, K. (2020). Common Vector Approach Based Image Gradients Computation for Edge Detection. In: Slamanig, D., Tsigaridas, E., Zafeirakopoulos, Z. (eds) Mathematical Aspects of Computer and Information Sciences. MACIS 2019. Lecture Notes in Computer Science(), vol 11989. Springer, Cham. https://doi.org/10.1007/978-3-030-43120-4_32

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  • DOI: https://doi.org/10.1007/978-3-030-43120-4_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43119-8

  • Online ISBN: 978-3-030-43120-4

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