Quality assessment metrics for edge detection and edge-aware filtering

A tutorial review

Diana Sadykova, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The quality assessment of edges in an image is an important topic as it helps to benchmark the performance of edge detectors, and edge-aware filters that are used in a wide range of image processing tasks. The most popular image quality metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR) and Structural similarity (SSIM) metrics for assessing and justifying the quality of edges. However, they do not address the structural and functional accuracy of edges in images with a wide range of natural variabilities. In this review, we provide an overview of all the most relevant performance metrics that can be used to benchmark the quality performance of edges in images. We identify four major groups of metrics and also provide a critical insight into the evaluation protocol and governing equations.

Original languageEnglish
Title of host publication2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2366-2369
Number of pages4
Volume2017-January
ISBN (Electronic)9781509063673
DOIs
Publication statusPublished - Nov 30 2017
Event2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India
Duration: Sep 13 2017Sep 16 2017

Conference

Conference2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
CountryIndia
CityManipal, Mangalore
Period9/13/179/16/17

Fingerprint

Edge detection
Image quality
Signal to noise ratio
Image processing
Detectors

Keywords

  • Edge detection
  • Edge preservation
  • Edge quality measures
  • Image metrics
  • Mean square error
  • PSNR
  • Sobel filters
  • SSIM

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Sadykova, D., & James, A. P. (2017). Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 2366-2369). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8126200

Quality assessment metrics for edge detection and edge-aware filtering : A tutorial review. / Sadykova, Diana; James, Alex Pappachen.

2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 2366-2369.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sadykova, D & James, AP 2017, Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 2366-2369, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 9/13/17. https://doi.org/10.1109/ICACCI.2017.8126200
Sadykova D, James AP. Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2366-2369 https://doi.org/10.1109/ICACCI.2017.8126200
Sadykova, Diana ; James, Alex Pappachen. / Quality assessment metrics for edge detection and edge-aware filtering : A tutorial review. 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2366-2369
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