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Multiscale edge detection using first-order derivative of anisotropic Gaussian kernels

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Abstract
Spatially scaled edges are ubiquitous in natural images. To better detect edges with heterogeneous widths, in this paper, we propose a multiscale edge detection method based on first-order derivative of anisotropic Gaussian kernels. These kernels are normalized in scale-space, yielding a maximum response at the scale of the observed edge, and accordingly, the edge scale can be identified. Subsequently, the maximum response and the identified edge scale are used to compute the edge strength. Furthermore, we propose an adaptive anisotropy factor of which the value decreases as the kernel scale increases. This factor improves the noise robustness of small-scale kernels while alleviating the anisotropy stretch effect that occurs in conventional anisotropic methods. Finally, we evaluate our method on widely used datasets. Experimental results validate the benefits of our method over the competing methods.
Keywords
Multiscale edge detection, Edge strength, First-order derivative of anisotropic Gaussian kernels, Scale-space, Noise robustness, RIDGE DETECTION, CLASSIFICATION, ALGORITHM, COLOR

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MLA
WANG, Gang, et al. “Multiscale Edge Detection Using First-Order Derivative of Anisotropic Gaussian Kernels.” JOURNAL OF MATHEMATICAL IMAGING AND VISION, vol. 61, no. 8, 2019, pp. 1096–111.
APA
WANG, G., Lopez-Molina, C., & De Baets, B. (2019). Multiscale edge detection using first-order derivative of anisotropic Gaussian kernels. JOURNAL OF MATHEMATICAL IMAGING AND VISION, 61(8), 1096–1111.
Chicago author-date
WANG, Gang, Carlos Lopez-Molina, and Bernard De Baets. 2019. “Multiscale Edge Detection Using First-Order Derivative of Anisotropic Gaussian Kernels.” JOURNAL OF MATHEMATICAL IMAGING AND VISION 61 (8): 1096–1111.
Chicago author-date (all authors)
WANG, Gang, Carlos Lopez-Molina, and Bernard De Baets. 2019. “Multiscale Edge Detection Using First-Order Derivative of Anisotropic Gaussian Kernels.” JOURNAL OF MATHEMATICAL IMAGING AND VISION 61 (8): 1096–1111.
Vancouver
1.
WANG G, Lopez-Molina C, De Baets B. Multiscale edge detection using first-order derivative of anisotropic Gaussian kernels. JOURNAL OF MATHEMATICAL IMAGING AND VISION. 2019;61(8):1096–111.
IEEE
[1]
G. WANG, C. Lopez-Molina, and B. De Baets, “Multiscale edge detection using first-order derivative of anisotropic Gaussian kernels,” JOURNAL OF MATHEMATICAL IMAGING AND VISION, vol. 61, no. 8, pp. 1096–1111, 2019.
@article{8634254,
  abstract     = {Spatially scaled edges are ubiquitous in natural images. To better detect edges with heterogeneous widths, in this paper, we propose a multiscale edge detection method based on first-order derivative of anisotropic Gaussian kernels. These kernels are normalized in scale-space, yielding a maximum response at the scale of the observed edge, and accordingly, the edge scale can be identified. Subsequently, the maximum response and the identified edge scale are used to compute the edge strength. Furthermore, we propose an adaptive anisotropy factor of which the value decreases as the kernel scale increases. This factor improves the noise robustness of small-scale kernels while alleviating the anisotropy stretch effect that occurs in conventional anisotropic methods. Finally, we evaluate our method on widely used datasets. Experimental results validate the benefits of our method over the competing methods.},
  author       = {WANG, Gang and Lopez-Molina, Carlos and De Baets, Bernard},
  issn         = {0924-9907},
  journal      = {JOURNAL OF MATHEMATICAL IMAGING AND VISION},
  keywords     = {Multiscale edge detection,Edge strength,First-order derivative of anisotropic Gaussian kernels,Scale-space,Noise robustness,RIDGE DETECTION,CLASSIFICATION,ALGORITHM,COLOR},
  language     = {eng},
  number       = {8},
  pages        = {1096--1111},
  title        = {Multiscale edge detection using first-order derivative of anisotropic Gaussian kernels},
  url          = {http://dx.doi.org/10.1007/s10851-019-00892-1},
  volume       = {61},
  year         = {2019},
}

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