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Recent Developments and the New Direction in Soft-Computing Foundations and Applications pp 411–418Cite as

A Quantitative Assessment of Edge Preserving Smoothing Filters for Edge Detection

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 361))

Abstract

Edge detection algorithms have traditionally utilized the Gaussian Linear Filter (GLF) for image smoothing. Although GLF has very good properties in removing noise and unwanted artifacts from an image, it is also known to remove many valid edges. To cope with this problem, edge preserving smoothing filters have been proposed and they have recently attracted increased attention. In this paper, we quantitatively compare three prominent edge preserving smoothing filters; namely, Bilateral Filter (BLF), Anisotropic Diffusion (AD) and Weighted Least Squares (WLS) with each other and with GLF in terms of their effects on the final detected edges using the precision/recall framework of the famous Berkeley Segmentation Dataset (BSDS 300). We conclude that edge preserving smoothing filters indeed improve the performance of the edge detectors, and of the filters compared, WLS yields the best performance with AD also outperforming the GLF.

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Authors and Affiliations

  1. Department of Computer Engineering, Anadolu University, Eskisehir, Turkey

    Huseyin Gunduz, Cihan Topal & Cuneyt Akinlar

Authors
  1. Huseyin Gunduz
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  2. Cihan Topal
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  3. Cuneyt Akinlar
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Corresponding author

Correspondence to Huseyin Gunduz .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  2. Machine Intelligence Institute, Iona College, New Rochelle, New York, USA

    Ronald R. Yager

  3. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

  5. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

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Gunduz, H., Topal, C., Akinlar, C. (2018). A Quantitative Assessment of Edge Preserving Smoothing Filters for Edge Detection. In: Zadeh, L., Yager, R., Shahbazova, S., Reformat, M., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_31

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  • DOI: https://doi.org/10.1007/978-3-319-75408-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75407-9

  • Online ISBN: 978-3-319-75408-6

  • eBook Packages: EngineeringEngineering (R0)

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