To improve the image edge detection accuracy and anti-noise performance, a new approach for image edge detection based on conformal phase is proposed. Firstly, the proposed approach can effectively improve the precision of edge detection and restrain the false edge and noise by using respectively the conformal monogenic signal which could express local structure of the image with different intrinsic dimensions and an exponential function to calculate the phase deviation. Secondly, it can reduce the complexity of the algorithm by taking advantage of the Poisson kernel of existence of analytic representation in spatial domain. To demonstrate the advantages, the proposed approach is compared with the existing methods?of phase congruency based edge?detection. The simulation experiment results show that the proposed approach can extract image edge more accurately, more completely, and more uniformly, with better robustness to noise and lower computational complexity.
石美红, 李青,赵雪青,乔冬冬. 一种基于保角相位的图像边缘检测新方法[J]. 电子与信息学报, 2015, 37(11): 2594-2600.
Shi Mei-hong, Li Qing, Zhao Xue-qing, Qiao Dong-dong. A New Approach for Image Edge Detection Based on Conformal Phase. JEIT, 2015, 37(11): 2594-2600.
Molina C, Baets B, and Bustince H. A framework for edge detection based on relief functions[J]. Information Sciences, 2014, 278(10): 127-140.
[2]
Ray K. Unsupervised edge detection and noise detection from a single image[J]. Pattern Recognition, 2013, 46(8): 2067-2077.
[3]
Hao Fei, Shi Jin-fei, Zhang Zhi-sheng, et al.. Canny edge detection enhancement by general auto-regression model and bi-dimensional maximum conditional entropy[J]. Optik, 2014, 125(15): 3946-3953.
[4]
Xu Qian, Varadarajan S, Chakrabarti C, et al.. A distributed canny edge detector: algorithm and FPGA implementation[J]. IEEE Transactions on Image Processing, 2014, 23(7): 2944-2960.
[5]
Morrone M, Ross J, Burr D, et al.. Mach bands are phase dependent[J]. Nature, 1986, 324(6094): 250-253.
[6]
Morrone M and Owens R. Feature detection from local energy[J]. Pattern Recognition Letters, 1987, 6(5): 303-313.
[7]
Kovesi P. Image features from phase congruency[J]. Videre: Journal of Computer Vision Research, 1999, 1(3): 1-26.
[8]
Liu De-lei, Xu Yong, Quan Yu-hui, et al.. Reduced reference image quality assessment using regularity of phase congruency[J]. Signal Processing: Image Communication, 2014, 29(8): 844-855.
[9]
Shojaeilangari S, Yau W, and Teoh E. A novel phase congruency based descriptor for dynamic facial expression analysis[J]. Pattern Recognition Letters, 2014, 49(11): 55-61.
Li Ming, Li De-ren, Fan Deng-ke, et al.. An automatic PC-SIFT-based registration of multi-source images from optical satellites[J]. Geomatics and Information Science of Wuhan University, 2015, 40(1): 64-70.
[11]
Wang Li-juan, Zhang Chang-sheng, Liu Zi-yu, et al.. Image feature detection based on phase congruency by Monogenic filters[C]. The 26th Chinese Control and Decision Conference, Changsha, 2014: 2033-2038.
[12]
Fleischmann O, Wietzke L, and Sommer G. Image analysis by conformal embedding[J]. Journal of Mathematical Imaging and Vision, 2011, 40(3): 305-325.
[13]
Felsberg M and Sommer G. The monogenic signal[J]. IEEE Transactions on Signal Processing, 2001, 49(12): 3136-3144.
[14]
Wietzke L, Sommer G, Schmaltz C, et al.. Differential geometry of monogenic signal representations[C]. Robot Vision: Second International Workshop, Auckland, 2008: 454-465.
[15]
Wietzke L and Sommer G. The conformal monogenic signal[C]. Pattern Recognition: 30th DAGM Symposium, Munich, 2008: 527-536.
[16]
Felsberg M. Low level image processing with the structure multivector[D]. [Ph.D. dissertation], Christian-Albert Kiel University, 2002: 95-100.
[17]
Felsberg M and Sommer G. The monogenic scale-space: a unifying approach to phase-based image processing in scale-space[J]. Journal of Mathematical Imaging and Vision, 2004, 21(1/2): 5-26.
[18]
Kovesi P. Phase preserving denoising of images[C]. The Australian Pattern Recognition Society Conference, Perth, 1999: 212-217.
Luo Ding, Zhao Rong-chun, Ci Lin-lin, et al.. Phase congruency based edge detection by Hilbert filters[J]. Journal of Image and Graphics, 2004, 9(2): 139-245.
