Research article Special Issues

Image edge detection based on singular value feature vector and gradient operator

  • Received: 18 March 2020 Accepted: 18 May 2020 Published: 22 May 2020
  • This paper presents an edge detection algorithm based on singular value eigenvector and gradient operator. In the proposed algorithm, the singular values of image blocks are first calculated, and the Sobel gradient template is extended to eight other directions. Then the gradient values of image pixels are determined according to the stability of the singular values of image blocks. The determination of gradient threshold is considered from both global and local aspects. After calculating the global and local gradient thresholds of the original image, the gradient threshold of the whole image is determined by weighting function. Then the edge pixels of the image are filtered according to the gradient threshold, and the edge information image of the original image is obtained. The experimental data show that the proposed algorithm can resist a certain degree of noise interference, and the accuracy and efficiency of edge extraction are better than other similar algorithms.

    Citation: Jiali Tang, Yan Wang, Chenrong Huang, Huangxiaolie Liu, Najla Al-Nabhan. Image edge detection based on singular value feature vector and gradient operator[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3721-3735. doi: 10.3934/mbe.2020209

    Related Papers:

  • This paper presents an edge detection algorithm based on singular value eigenvector and gradient operator. In the proposed algorithm, the singular values of image blocks are first calculated, and the Sobel gradient template is extended to eight other directions. Then the gradient values of image pixels are determined according to the stability of the singular values of image blocks. The determination of gradient threshold is considered from both global and local aspects. After calculating the global and local gradient thresholds of the original image, the gradient threshold of the whole image is determined by weighting function. Then the edge pixels of the image are filtered according to the gradient threshold, and the edge information image of the original image is obtained. The experimental data show that the proposed algorithm can resist a certain degree of noise interference, and the accuracy and efficiency of edge extraction are better than other similar algorithms.



    加载中


    [1] H. Zhang, Z. Duan, Z. Zhu, Y. Wang, Fast moving object segmentation based on active contours, J. Comput., 7 (2012), 863-869.
    [2] Z. Pan, C. N. Yang, V. S. Sheng, N. Xiong, W. Meng, Machine learning for wireless multimedia data security, Secur. Commun. Networks, 2019 (2019).
    [3] T. Ma, H. Rong, Y. Hao, J. Cao, Y. Tian, M. A. Al-Rodhaan, A Novel Sentiment Polarity Detection Framework for Chinese, IEEE Trans. Affective Comput., 2019 (2019).
    [4] Y. Tian, M. M. Kaleemullah, M. A. Rodhaan, B. Song, A. Al-Dhelaan, T. Ma, A privacy preserving location service for cloud-of-things system, J. Parallel Distrib. Comput., 123 (2019), 215-222. doi: 10.1016/j.jpdc.2018.09.005
    [5] B. Song, M. M. Hassan, A. Alamri, A. Alelaiwi, Y. Tian, M. Pathan, et al., A two-stage approach for task and resource management in multimedia cloud environment, Computing, 98 (2016), 119-145.
    [6] S. Mallat, W. L. Hwang, Singularity detection and processing with wavelets, IEEE Trans. Inf. Theory, 38 (1992), 617-643. doi: 10.1109/18.119727
    [7] W. B. Wei, X. T. Rui, Study on edge detection method, Comput. Eng. Appl., 42 (2006), 88-91.
    [8] Y. Tian, B. Song, M. Al-Rodhaan, C. R. Huang, M. A. Al-Dhelaan, A. Al-Dhelaan, et al., A stochastic location privacy protection scheme for edge computing, Math. Biosci. Eng., 17 (2020), 2636-2649.
    [9] P. Selvakumar, S. Hariganesh, The performance analysis of edge detection algorithms for image processing, IEEE International Conference on Computing Technologies and Intelligent Data Engineering, 2016, 1-5. Available from: https://ieeexplore.ieee.org/abstract/document/7725371.
    [10] Y. Li, X. Chang, J. Chang, Image edge detection based on Gaussian mixture model in non-subsampled contourlet domain, J. Electr. Comput. Eng., 2016 (2016), 4125909.
    [11] L. Zhang, P. Bao, Edge Detection by Scale Multiplication in Wavelet Domain, Pattern Recognit. Lett., 23 (2002), 1771-1784. doi: 10.1016/S0167-8655(02)00151-4
    [12] F. Y. Wang, M. Chen, Q. S. Fei, The improved Method for Image edge detection based on wavelet Transform with Modulus Maxima, Adv. Mat. Res., 850 (2014), 897-900.
    [13] S. Rani, A novel mathematical morphology based edge detection method for medical images, CSI Trans. ICT, 4 (2016), 217-225. doi: 10.1007/s40012-016-0092-1
    [14] M. Raman, A. Himanshu, Study and comparison of various image edge detection techniques, Int. J. Image Process., 3 (2009), 1-11. doi: 10.1049/iet-ipr:20080080
    [15] E. Nadernejad, S. Sharifzadeh, H. Hassanpour, Edge detection techniques: Evaluation and comparisons, Appl. Math. Sci., 2 (2008), 1507-1520.
    [16] M. Samuel, M. Mohamad, S. M. Saad, M. Hussein, Development of edge-based lane detection algorithm using image processing, JOIV Int. J. Inf. Visualization, 2 (2018), 19-22. doi: 10.30630/joiv.2.1.101
    [17] O. P. Verma, N. Agrawal, S. Sharma, An optimal edge detection using modified artificial bee colony algorithm, Proceedings of the National Academy of Sciences India, 2016, 1-12. Available from: https://link.springer.com/article/10.1007/s40010-015-0256-7.
    [18] S. Mallat, S. Zhong, Characterization of signals from multiscale edges, IEEE Trans. Pattern Anal. Mach. Intell., 14 (1992), 710-732. doi: 10.1109/34.142909
    [19] W. F. Ma, C. X. Deng, An improved wavelet multi-scale edge detection algorithm, IEEE International Conference on Wavelet Analysis and Pattern Recognition, 2010,302-306. Available from: https://ieeexplore.ieee.org/abstract/document/6294797.
    [20] R. Silva, R. Minetto, W. R. Schwartz, H. Pedrini, Adaptive edge-preserving image denoising using wavelet transforms, Pattern Anal. Appl., 16 (2013), 567-580. doi: 10.1007/s10044-012-0266-x
    [21] C. I. Gonzalez, P. Melin, J. R. Castro, O. Mendoza, O. Castillo, An improved sobel edge detection method based on generalized type-2 fuzzy logic, Soft Comput., 20 (2016), 773-784. doi: 10.1007/s00500-014-1541-0
    [22] D. G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision, 60 (2004), 91-110. doi: 10.1023/B:VISI.0000029664.99615.94
    [23] N. Nausheen, A. Seal, P. Khanna, S. Halder, A FPGA based implementation of Sobel edge detection, Microprocess. Microsyst., 56 (2018), 84-91. doi: 10.1016/j.micpro.2017.10.011
    [24] A. Fabijańska, A survey of subpixel edge detection methods for images of heat-emitting metal specimens, Int. J. Appl. Math. Comput. Sci., 22 (2012), 695-710. doi: 10.2478/v10006-012-0052-3
    [25] T. Chen, X. Sun, H. Han, X. You, Image edge detection based on ACO-PSO algorithm, Image, 6 (2015), 47-54.
    [26] G. T. Shrivakshan, C. Chandrasekar, A Comparison of Various Edge Detection Techniques Used in Image Processing, Int. J. Comput. Sci. Issues, 9 (2012), 269-276.
    [27] R. Liu, T. Tan, Digital image watermarking method based on SVD, Acta Electron. Sin., 29 (2001), 168-171.
    [28] F. Guo, Y. Yang, B. Chen, L. Guo, A novel multi-scale edge detection technique based on wavelet analysis with application in multiphase flows, Powder Technol., 202 (2010), 171-177. doi: 10.1016/j.powtec.2010.04.035
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4596) PDF downloads(328) Cited by(13)

Article outline

Figures and Tables

Figures(8)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog