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.
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Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)
Atick, J.J., Redlich, A.N.: What does the retina know about natural scenes? Neural Comput. 4(2), 196–210 (1992)
Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1485–1490 (2005)
Basu, M.: Gaussian-based edge-detection methods—a survey. IEEE Trans. Syst. Man Cybern. C-Appl. Rev. 32(3), 252–260 (2002)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Coleman, S.A., Scotney, B.W., Suganthan, S.: Edge detecting for range data using Laplacian operators. IEEE Trans. Image Process. 19(11), 2814–2824 (2010)
Ding, L., Goshtasby, A.: On the Canny edge detector. Pattern Recognit. 34(3), 721–725 (2001)
Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2015)
Elder, J.H., Zucker, S.W.: Local scale control for edge detection and blur estimation. IEEE Trans. Pattern Anal. Mach. Intell. 20(7), 699–716 (1998)
Goldberg, A.V., Kennedy, R.: An efficient cost scaling algorithm for the assignment problem. Math. Program. 71(2), 153–177 (1995)
Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour detection based on nonclassical receptive field inhibition. IEEE Trans. Image Process. 12(7), 729–739 (2003)
Guerra, C., Jurio, A., Bustince, H., Lopez-Molina, C.: Multichannel generalization of the upper-lower edge detector using ordered weighted averaging operators. J. Intell. Fuzzy Syst. 27(3), 1433–1443 (2014)
Jacob, M., Unser, M.: Design of steerable filters for feature detection using Canny-like criteria. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1007–1019 (2004)
Koschan, A., Abidi, M.: Detection and classification of edges in color images. IEEE Signal Process. Mag. 22(1), 64–73 (2005)
Li, X., Chen, T.: Nonlinear diffusion with multiple edginess thresholds. Pattern Recognit. 27(8), 1029–1037 (1994)
Li, Y., Wang, S., Tian, Q., Ding, X.: A survey of recent advances in visual feature detection. Neurocomputing 149, 736–751 (2015)
Li, Z., Ahmed, E., Eltawil, A.M., Cetiner, B.A.: A beam-steering reconfigurable antenna for WLAN applications. IEEE Trans. Antennas Propag. 63(1), 24–32 (2015)
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 117–156 (1998)
Lindeberg, T.: Scale selection properties of generalized scale-space interest point detectors. J. Math. Imaging Vis. 46(2), 177–210 (2013)
Lindeberg, T.: Image matching using generalized scale-space interest points. J. Math. Imaging Vis. 52(1), 3–36 (2015)
Liu, Y., Cheng, M.-M., Hu, X., Wang, K., Bai, X.: Richer convolutional features for edge detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5872–5881 (2017)
Lopez-Molina, C., Bustince, H., De Baets, B.: Separability criteria for the evaluation of boundary detection benchmarks. IEEE Trans. Image Process. 25(3), 1047–1055 (2016)
Lopez-Molina, C., De Baets, B., Bustince, H.: Quantitative error measures for edge detection. Pattern Recognit. 46(4), 1125–1139 (2013)
Lopez-Molina, C., Montero, J., Bustince, H., De Baets, B.: Self-adapting weighted operators for multiscale gradient fusion. Inf. Fusion 44, 136–146 (2018)
Lopez-Molina, C., Vidal-Diez de Ulzurrun, G., Baetens, J.M., Van Den Bulcke, J., De Baets, B.: Unsupervised ridge detection using second order anisotropic Gaussian kernels. Signal Process. 116, 55–67 (2015)
Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B 207, 187–217 (1980)
Martin, D.R.: An empirical approach to grouping and segmentation. Ph.D. thesis, University of California, Berkeley (2003)
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)
McIlhagga, W.: The Canny edge detector revisited. Int. J. Comput. Vis. 91(3), 251–261 (2011)
Pan, X., Ye, Y., Wang, J., Gao, X., He, C., Wang, D., Jiang, B., Li, L.: Complex composite derivative and its application to edge detection. SIAM J. Imaging Sci. 7(4), 2807–2832 (2014)
Perona, P., Malik, J.: Detecting and localizing edges composed of steps, peaks and roofs. In: Proceedings of the International Conference on Computer Vision, pp. 52–57 (1990)
Prewitt, J.M.: Object enhancement and extraction. Picture Process. Psychopictorics 10(1), 15–19 (1970)
Ray, K.: Unsupervised edge detection and noise detection from a single image. Pattern Recognit. 46(8), 2067–2077 (2013)
Roberts, L.G.: Machine perception of three-dimensional solids. In: Optical and Electro-Optical Information Processing, pp. 159–197. MIT Press (1965)
Rosenfeld, A.: A nonlinear edge detection technique. Proc. IEEE 58(5), 814–816 (1970)
Rosenfeld, A., Thurston, M.: Edge and curve detection for visual scene analysis. IEEE Trans. Comput. 20(5), 562–569 (1971)
Shui, P.L., Wang, F.P.: Anti-impulse-noise edge detection via anisotropic morphological directional derivatives. IEEE Trans. Image Process. 26(10), 4962–4977 (2017)
Shui, P.L., Zhang, W.C.: Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels. Pattern Recognit. 45(2), 806–820 (2012)
Shui, P.L., Zhang, W.C.: Corner detection and classification using anisotropic directional derivative representations. IEEE Trans. Image Process. 22(8), 3204–3218 (2013)
Sobel, I.: Camera models and machine perception. Ph.D. thesis, Stanford University (1970)
Torre, V., Poggio, T.A.: On edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(2), 147–163 (1986)
Wang, F.P., Shui, P.L.: Noise-robust color edge detector using gradient matrix and anisotropic Gaussian directional derivative matrix. Pattern Recognit. 52, 346–357 (2016)
Wang, G., De Baets, B.: Edge detection based on the fusion of multiscale anisotropic edge strength measurements. In: Proceedings of the Conference of the European Society for Fuzzy Logic and Technology, vol. 3, pp. 530–536 (2017)
Wang, G., De Baets, B.: Superpixel segmentation based on anisotropic edge strength. J. Imaging 5(6), 57 (2019)
Wang, G., Lopez-Molina, C., De Baets, B.: Blob reconstruction using unilateral second order Gaussian kernels with application to high-ISO long-exposure image denoising. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4817–4825 (2017)
Wang, G., Lopez-Molina, C., Vidal-Diez de Ulzurrun, G., De Baets, B.: Noise-robust line detection using normalized and adaptive second-order anisotropic Gaussian kernels. Signal Process. 160, 252–262 (2019)
Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 25, 3–18 (2017)
Xu, Q., Varadarajan, S., Chakrabarti, C., Karam, L.J.: A distributed Canny edge detector: algorithm and FPGA implementation. IEEE Trans. Image Process. 23(7), 2944–2960 (2014)
Yang, K.F., Gao, S.B., Guo, C.F., Li, C.Y., Li, Y.J.: Boundary detection using double-opponency and spatial sparseness constraint. IEEE Trans. Image Process. 24(8), 2565–2578 (2015)
Zhang, W., Zhao, Y., Breckon, T.P., Chen, L.: Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels. Pattern Recognit. 63, 193–205 (2017)
Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Proceedings of the European Conference on Computer Vision, pp. 391–405 (2014)
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Wang, G., Lopez-Molina, C. & De Baets, B. Multiscale Edge Detection Using First-Order Derivative of Anisotropic Gaussian Kernels. J Math Imaging Vis 61, 1096–1111 (2019). https://doi.org/10.1007/s10851-019-00892-1
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DOI: https://doi.org/10.1007/s10851-019-00892-1