Zhang, W. and Zhao, Y. and Breckon, T.P. and Chen, L. (2016) 'Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels.', Pattern recognition., 63 (8). pp. 193-205.
This paper presents a novel noise robust edge detector based upon the automatic anisotropic Gaussian kernels (ANGKs), which also addresses the current problem that the seminal Canny edge detector may miss some obvious crossing edge details. Firstly, automatic ANGKs are designed according to the noise suppression, edge resolution and localization precision, which also conciliate the conflict between them. Secondly, reasons why cross-edge points are missing from Canny detector results using isotropic Gaussian kernel are analyzed. Thirdly, the automatic ANGKs are used to smooth image and a revised edge extraction method is used to extract edges. Finally, the aggregate test receiver-operating-characteristic (ROC) curves and Pratt's Figure of Merit (FOM) are used to evaluate the proposed detector against state-of-the-art edge detectors. The experiment results show that the proposed algorithm can obtain better performance for noise-free and noisy images.
|Full text:||(AM) Accepted Manuscript|
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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|Publisher Web site:||http://dx.doi.org/10.1016/j.patcog.2016.10.008|
|Publisher statement:||© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/|
|Date accepted:||05 October 2016|
|Date deposited:||11 January 2017|
|Date of first online publication:||06 October 2016|
|Date first made open access:||06 October 2017|
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