WILLIAMS, Ian, Nicholas BOWRING and David SVOBODA. A performance evaluation of statistical tests for edge detection in textured images. Computer Vision and Image Understanding. Elsevier, 2014, vol. 122, May 2014, p. 115-130. ISSN 1077-3142. Available from: https://dx.doi.org/10.1016/j.cviu.2014.02.009.
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Basic information
Original name A performance evaluation of statistical tests for edge detection in textured images
Authors WILLIAMS, Ian (826 United Kingdom of Great Britain and Northern Ireland), Nicholas BOWRING (826 United Kingdom of Great Britain and Northern Ireland) and David SVOBODA (203 Czech Republic, guarantor, belonging to the institution).
Edition Computer Vision and Image Understanding, Elsevier, 2014, 1077-3142.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 1.540
RIV identification code RIV/00216224:14330/14:00075212
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1016/j.cviu.2014.02.009
UT WoS 000334394900011
Keywords in English Edge detection; Statistical tests; Textured images; Histological images; Performance measures
Tags cbia-web
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 27/4/2015 03:20.
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 and Kolmogorov Smirnov 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 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.
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