Edge detection filter based on Mumford-Shah green function

Mahmoodi, Sasan (2012) Edge detection filter based on Mumford-Shah green function. SIAM Journal on Imaging Sciences, 5, (1), 343-365. (doi:10.1137/100811349).


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In this paper, we propose an edge detection algorithm based on the Green function associated with Mumford-Shah (M-S) segmentation model. This Green function has a singularity at its center. A regularization method is therefore proposed here to obtain an edge detection filter known here as Bessel filter. This filter is robust in the presence of noise and its implementation is simple. It is demonstrated here that this filter detects edges particularly in the case of curved boundaries and sharp corners, more accurately than popular filters in the recent literature. A mathematical argument is also provided to prove that the gradient magnitude of the convolved image with this filter has local maxima in discontinuities of the original image. The Bessel filter enjoys better overall performance (the product of the detection performance and localization indices) in Canny-like criteria than the state of art filters in the literature. Quantitative and qualitative evaluations of the edge detection algorithms investigated in this paper on synthetic and real world benchmark images confirm the theoretical results presented here, indicating the superiority of the Bessel filter over the popular edge detection filters. The numerical complexity of the algorithm proposed here is as low as any convolution-based edge detection algorithm.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1137/100811349
ISSNs: 1936-4954 (electronic)
Related URLs:
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
ePrint ID: 271513
Accepted Date and Publication Date:
8 March 2012Published
Date Deposited: 02 Sep 2010 11:48
Last Modified: 31 Mar 2016 14:19
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/271513

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