一种基于保角相位的图像边缘检测新方法

石美红; 李青; 赵雪青; 乔冬冬

doi:10.11999/JEIT150364

一种基于保角相位的图像边缘检测新方法

doi: 10.11999/JEIT150364

基金项目:

国家科技支撑计划基金(2014BAF07B01)和中国纺织工业联合会科技项目(2014066)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-03-27
- 修回日期: 2015-06-29
- 刊出日期: 2015-11-19

A New Approach for Image Edge Detection Based on Conformal Phase

Funds:

The National Science Technology Support Program of China (2014BAF07B01)

摘要

摘要: 为了提高边缘检测精确度与抗噪性能，该文提出一种基于保角相位的图像边缘检测新方法。该方法首先利用保角单演信号能够表达不同本征维数的图像局部结构的特点，采用指数函数计算相位偏差，有效地抑制了相位一致模型边缘检测中产生的伪边缘和噪声，提高了边缘检测的精确度；其次，利用Poisson核在空域中有解析表示的优势，降低了算法复杂度。仿真实验结果表明，与现有的相位一致性图像边缘检测方法相比，该方法提取的图像边缘更精确、更完整、更均匀，对噪声具有更好的鲁棒性，同时，计算复杂度较低。
- 图像处理 /
- 边缘检测 /
- 相位一致性 /
- 保角单演信号 /
- 本征维数
Abstract: 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.
- Image processing /
- Edge detection /
- Phase congruency /
- Conformal monogenic signal /
- Intrinsic dimensionality

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参考文献(21)

Molina C, Baets B, and Bustince H. A framework for edge detection based on relief functions[J]. Information Sciences, 2014, 278(10): 127-140.

Ray K. Unsupervised edge detection and noise detection from a single image[J]. Pattern Recognition, 2013, 46(8): 2067-2077.

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.

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.

Morrone M, Ross J, Burr D, et al.. Mach bands are phase dependent[J]. Nature, 1986, 324(6094): 250-253.

Morrone M and Owens R. Feature detection from local energy[J]. Pattern Recognition Letters, 1987, 6(5): 303-313.

Kovesi P. Image features from phase congruency[J]. Videre: Journal of Computer Vision Research, 1999, 1(3): 1-26.

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.

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.

李明, 李德仁, 范登科, 等. 利用PC-SIFT的多源光学卫星影像自动配准方法[J]. 武汉大学学报.(信息科学版), 2015, 40(1): 64-70.

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.

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.

Fleischmann O, Wietzke L, and Sommer G. Image analysis by conformal embedding[J]. Journal of Mathematical Imaging and Vision, 2011, 40(3): 305-325.

Felsberg M and Sommer G. The monogenic signal[J]. IEEE Transactions on Signal Processing, 2001, 49(12): 3136-3144.

Wietzke L, Sommer G, Schmaltz C, et al.. Differential geometry of monogenic signal representations[C]. Robot Vision: Second International Workshop, Auckland, 2008: 454-465.

Wietzke L and Sommer G. The conformal monogenic signal[C]. Pattern Recognition: 30th DAGM Symposium, Munich, 2008: 527-536.

Felsberg M. Low level image processing with the structure multivector[D]. [Ph.D. dissertation], Christian-Albert Kiel University, 2002: 95-100.

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.

Kovesi P. Phase preserving denoising of images[C]. The Australian Pattern Recognition Society Conference, Perth, 1999: 212-217.

罗丁, 赵荣椿, 慈林林, 等. 基于Hilbert滤波器对的相位一致性边缘检测方法[J]. 中国图象图形学报, 2004, 9(2): 139-145.

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.

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