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Open AccessArticle

Superpixel Segmentation Based on Anisotropic Edge Strength

KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, B-9000 Gent, Belgium
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J. Imaging 2019, 5(6), 57; https://doi.org/10.3390/jimaging5060057
Received: 12 April 2019 / Revised: 23 May 2019 / Accepted: 29 May 2019 / Published: 5 June 2019
(This article belongs to the Special Issue Soft Computing for Edge Detection)
Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection. View Full-Text
Keywords: edge strength; first derivative of anisotropic Gaussian kernel; superpixel segmentation; distance measure; graph-based method edge strength; first derivative of anisotropic Gaussian kernel; superpixel segmentation; distance measure; graph-based method
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Wang, G.; De Baets, B. Superpixel Segmentation Based on Anisotropic Edge Strength. J. Imaging 2019, 5, 57.

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