Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

doi:10.19682/j.cnki.1005-8885.2018.0022

中国邮电高校学报(英文) ›› 2018, Vol. 25 ›› Issue (3): 55-64.doi: 10.19682/j.cnki.1005-8885.2018.0022

• Artificial Intelligence • 上一篇下一篇

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

虎玲,常霞,钱伟

北方民族大学

收稿日期:2018-01-02 修回日期:2018-05-21 出版日期:2018-06-29 发布日期:2018-06-30
通讯作者: 常霞 E-mail:changxia0104@163.com
基金资助:
中国国家自然科学基金;宁夏高等学校一流学科建设（数学学科）资助项目;北方民族大学研究生创新项目

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

Received:2018-01-02 Revised:2018-05-21 Online:2018-06-29 Published:2018-06-30
Supported by:
National Nature Science Foundation of China;First-Class Disciplines Foundation of Ningxia;Graduate Innovation Project of North Minzu University

摘要/Abstract

摘要： Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the field of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform (NSCT). The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima (MM) algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network (PCNN). The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.

关键词: edge detection, modulus maxima, pulse coupled neural network, wavelet transform, non-subsampled contourlet transform

Abstract: Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the field of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform (NSCT). The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima (MM) algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network (PCNN). The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.

Key words: edge detection, modulus maxima, pulse coupled neural network, wavelet transform, non-subsampled contourlet transform

中图分类号:

TP751

参考文献

Liu J Y, Tang Q J, Yang W, et al. Defects’ geometric feature recognition based on infrared image edge detection. Infrared Physics & Technology, 2014,

67: 387–390.

2. Zheng H B, Zhu X C. Sampling adaptive block compressed sensing reconstruction algorithm for images based on edge detection. The Journal of

China Universities of Posts and Telecommunications, 2013, 20(3): 97–103.

3. Guo Y Y, He D J, Song H B. Region detection of lesion area of knee based on colour edge detection and bilateral projection. Biosystems Engineering,

(To be published).

4. Kumar V, Asati A, Gupta A. Hardware implementation of a novel edge-map generation technique for pupil detection in NIR images. Engineering

Science and Technology: An International Journal, 2017, 20(2): 694–704.

5. Price A, Pyke J, Ashiri D, et al. Real time object detection for an unmanned aerial vehicle using an FPGA based vision system. Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA'06), May 15-19, 2006, Orlando, FL, USA. Piscataway, NJ, USA: IEEE, 2006: 2854–2859.

6. Shahabi H H, Ratnam M M. In-cycle detection of built-up edge (BUE) from 2-D images of cutting tools using machine vision. The International Journal of

Advanced Manufacturing Technology, 2010, 46(9/10/11/12): 1179–1189.

7. Patel A, Patel A. Performance enhancement in image edge detection technique. Proceedings of the 2016 International Conference on Signal and Information Processing (IConSIP'16), Oct 6-8, 2016, Vishnupuri, India. Piscataway, NJ, USA: IEEE, 2017, 5p.

8. Brannock E, Weeks M. A synopsis of recentwork in edge detection using the DWT. Proceedings of the IEEE SoutheastCon 2008, Apr 3-6, 2008, Huntsville, AL, USA. Piscataway, NJ, USA: IEEE, 2008: 515–520.

9. Mallat S, Hwang W L. Singularity detection and processing with wavelets. IEEE Transactions on Information Theory, 2002, 38(2): 617–643.

10. Do M N, Vetterli M. The contourlet transform: An efficient directional multiresolution image representation. IEEE Transactions on Image

Processing, 2005, 14(12): 2091–2106.

11. Bamberger R H, Smith M J T. A filter bank for the directional decomposition of images: Theory and design. IEEE Transactions on Signal Processing, 1992, 40(4): 882–893.

12. Da Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform: Theory, Design, and applications. IEEE Transactions on Image Processing,

2006, 15(10): 3089–3101.

13. Wang Z B, Ma Y D, Cheng F Y, et al. Review of pulse-coupled neural network. Image and Vision Computing, 2010, 28: 5–13.

14. Tang X L, Tao Z Y, Tang P S, et al. Edge detection method based on PCNN. Proceedings of the 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing (ICCWAMTIP'14), Dec 19-21, 2014, Chengdu, China. Piscataway, NJ, USA: IEEE, 2014: 169–171.

15. Chen Y L, Ma Y D, Kim D H, et al. Region-based object recognition by color segmentation using a simplified PCNN. IEEE Transactions on Neural

Networks and Learning Systems, 2015, 26(8): 1682–1697.

16. Xu G Z, Li C L, Zhao J J, et al. Multiplicative decomposition based image contrast enhancement method using PCNN factoring mode. Proceedings of the 11th World Congress on Intelligent Control and Automation (WCICA'15), Jun 29-Jul 4, 2014, Shenyang, China. Piscataway, NJ, USA: IEEE, 2015: 1511–1516.

17. Johnson J L, Padgett M L. PCNN models and applications. IEEE Transactions on Neural Networks, 1999, 10(3): 480–498.

18. Tong Y, Zhao M R, Wei Z L, et al. Synthetic aperture radar image nonlinear enhancement algorithm based on NSCT transform. Physical Communication, 2014, 13C: 239–243.

19. Mallat S, Zhong S F. Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(7): 710–732.

20. Lo T Y, Sim K S, Tso C P, et al. Improvement to the scanning electron microscope image adaptive Canny optimization colorization by pseudo-mapping. Scanning, 2015, 36(5): 530–539.

21. Li Y, Chang X, Chang J. Image edge detection based on Gaussian mixture model in nonsubsampled contourlet domain. Journal of Electrical and Computer Engineering, 2016: Article 4125909.

22. Abdou I E, Pratt W K. Quantitative design and evaluation of enhancement/thresholding edge detectors. Proceedings of the IEEE, 1979, 67(5): 753–763.

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

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