Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels

Abstract

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. 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.

Superpixel Segmentation Based on Anisotropic Edge Strength

“…As an illustration, Figure 1b displays an FDAG kernel, which is visually different from the corresponding isotropic kernel shown in Figure 1a. Existing FDAG-based methods [19] build a bank of FDAG kernels by setting the anisotropy factor and the direction with possible values. Subsequently, a family of responses is obtained by convolving each kernel with the image.…”

“…com/ASD) [44], and the neuronal structures in electron microscopy stacks dataset (NSEMS; http://brainiac2.mit.edu/isbi_challenge/home) [45]. In addition, in order to explore the impact of different kinds of edge strength on the segmentation accuracy, we also tested the SH methods that incorporate edge strength obtained by the structured forest edge (SH+SFE) method [28], the sparseness-constrained color-opponency (SH+SCO) method [26], the automated anisotropic Gaussian kernel (SH+AAGK) method [19], and the surrounded-modulation edge detection (SH+SED) method [27]. Furthermore, we also selected the widely used SLIC method [8] for comparison.…”

A new edge detection approach via neutrosophy based on maximum norm entropy

“…al., 2016 ), ant colony optimization ( Liu. & Fang, 2015 ), Cellular Automata ( Amrogowicz et al, 2016 ), Anisotropic Gaussian Kernels ( Zhang et al, 2017b ), and different edge detection approaches ( Sun et al, 2016 ;Zhang et al, 2017a ;Zareizadeh et. al., 2013 ) have also been proposed in the literature.…”

Numerical Simulation of Two Dimensional Flows in Yazidang Reservoir

“…Firstly, Google Earth software is used to obtain the regional image of the reservoir, and multiple local images are combined into a complete wide-field-of-view of the reservoir by means of image stitching [1][2][3]. Secondly, the edge-detection method [4][5][6][7] is used to detect the water edge in the image. Then the water edge information is extracted and saved as relevant files.…”