A performance evaluation of statistical tests for edge detection in textured images

Williams, I. and Bowring, N. and Svoboda, D. (2014) A performance evaluation of statistical tests for edge detection in textured images. Computer Vision and Image Understanding, 122. pp. 115-130. ISSN 10773142 (ISSN)

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Abstract

This work presents an objective performance analysis of statistical tests for edge detection which are suitable for textured or cluttered images. The tests are subdivided into two-sample parametric and non-parametric tests and are applied using a dual-region based edge detector which analyses local image texture difference. Through a series of experimental tests objective results are presented across a comprehensive dataset of images using a Pixel Correspondence Metric (PCM). The results show that statistical tests can in many cases, outperform the Canny edge detection method giving robust edge detection, accurate edge localisation and improved edge connectivity throughout. A visual comparison of the tests is also presented using representative images taken from typical textured histological data sets. The results conclude that the non-parametric Chi Square (χ2) and Kolmogorov Smirnov (KS) statistical tests are the most robust edge detection tests where image statistical properties cannot be assumed a priori or where intensity changes in the image are nonuniform and that the parametric Difference of Boxes (DoB) test and the Student's t-test are the most suitable for intensity based edges. Conclusions and recommendations are finally presented contrasting the tests and giving guidelines for their practical use while finally confirming which situations improved edge detection can be expected. © 2014 Elsevier Inc. All rights reserved.

Item Type: Article
Uncontrolled Keywords: Edge detection, Histological images, Performance measures, Statistical tests, Textured images, Edge detection, Canny edge detection, Histological images, Non-parametric test, Performance analysis, Performance measure, Robust edge detection, Statistical properties, Textured images, Statistical tests
Subjects: G400 Computer Science
Divisions: UoA Collections > UoA11: Computer Science and Informatics
Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Digital Media Technology
Depositing User: Yasser Nawaz
Date Deposited: 19 Jul 2016 14:35
Last Modified: 19 Jul 2016 14:35
URI: http://www.open-access.bcu.ac.uk/id/eprint/2011

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