An adaptive tensor voting algorithm combined with texture spectrum
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1. School of Information and Electrical Engineering, Ludong University, Yantai 264025, China;;2. School of Science, Nanjing University of Science & Technology, Nanjing 210094, China

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This work has been supported by the National Natural Science Foundation of China (No.61471185), the Joint Special Fund of Shandong Province Natural Science Foundation (No.ZR2013FL008), and the Project of Shandong Province Higher Educational Science and Technology Program (No.J14LN20).

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    Abstract:

    An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

    Reference
    [1] W. S. Tong, C. K. Tang and G. Medioni, First Order Tensor Voting, and Application to 3-D Scale Analysis, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 175 (2001).
    [2] P. Mordohai and G. Medioni, Journal of Machine Learning Research 11, 411 (2010).
    [3] Duan Fen-fang, Shao Feng, Jiang Gang-yi, Yu Mei and Li Fu-cui, Journal of Optoelectronics.Laser 25, 192 (2014). (in Chinese)
    [4] Li Wei-hong, Chen Long and Gong Wei-guo, Journal of Optoelectronics.Laser 25, 558 (2014). (in Chinese)
    [5] M. Kulkarni and A. N. Rajagopalan, Tensor Voting Based Foreground Object Extraction, National Confe- rence on Computer Vision, Pattern Recognition, Image Processing and Graphics, 86 (2011).
    [6] R. Hariharan and A. N. Rajagopalan, IEEE Transactions on Image Processing 21, 3323 (2012).
    [7] A. Mukherjee, B. Jenkins,C. Fang, R. J. Radke,G. Banker and B. Roysam, Medical Image Analysis 15, 354 (2011).
    [8] Jia-Ya Jia and Chi-Keung Tang, IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 36 (2005).
    [9] R. Hariharan and A. N. Rajagopalan, IEEE Transactions on Image Processing 21, 3323 (2012).
    [10] M. K. Park, S. J. Lee and K. H. Lee, Graphical Models 74, 197 (2012).
    [11] R. Lopes, P. Dubois, I. Bhouri, M. H. Bedoui, S. Maouche and N. Betrouni, Pattern Recognition 44, 1690 (2011).
    [12] Zhenhua Guo and Zhang D., IEEE Transactions on Image Processing 19, 1657 (2010).
    [13] G. H. Liu, Z. Y. Li, L. Zhang and Y. Xu, Pattern Recognition 44, 2132 (2011).
    [14] M. Sezgin and B. Sankur, Journal of Electronic Imaging 13, 146 (2004).
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WANG Gang, SU Qing-tang, Lü Gao-huan, ZHANG Xiao-feng, LIU Yu-huan, HE An-zhi. An adaptive tensor voting algorithm combined with texture spectrum[J]. Optoelectronics Letters,2015,11(1):73-76

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  • Received:September 28,2014
  • Online: November 26,2015
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