http://iet.metastore.ingenta.com
1887

access icon openaccess Entropy-based feature extraction algorithm for stone carving character detection

  • XML
    73.2333984375Kb
  • PDF
    2.2998361587524414MB
  • HTML
    74.1455078125Kb
Loading full text...

Full text loading...

/deliver/fulltext/joe/2018/16/JOE.2018.8318.html;jsessionid=4fgc3vm4rmdf2.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fjoe.2018.8318&mimeType=html&fmt=ahah

References

    1. 1)
      • 1. Yuan, Y., Zou, W., Zhao, Y., et al: ‘A robust and efficient approach to license plate detection’, IEEE Trans. Image Process., 2017, PP, (99), pp. 11.
    2. 2)
      • 2. Wang, L., Raghavan, H., Cardie, C., et al: ‘Query-focused opinion summarization for user-generated content’. Int. Conf. Computational Linguistics, Dublin, Ireland, August 2014, p. 315.
    3. 3)
      • 3. Li, H., Doermann, D., Kia, O.: ‘Automatic text detection and tracking in digital video’ (IEEE Press, 2000), pp. 147156.
    4. 4)
      • 4. Yin, X.C., Yin, X., Huang, K., et al: ‘Robust text detection in natural scene images’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 36, (5), pp. 970983.
    5. 5)
      • 5. Epshtein, B., Ofek, E., Wexler, Y.: ‘Detecting text in natural scenes with stroke width transform’. IEEE Computer Vision and Pattern Recognition, San Francisco, USA, June 2010, pp. 29632970.
    6. 6)
      • 6. ICDAR2017 robust reading competition’, http://mclab.eic.hust.edu.cn/ic 4 April 2018.
    7. 7)
      • 7. Ye, Q., Doermann, D.: ‘Text detection and recognition in imagery: a survey’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (7), pp. 14801500.
    8. 8)
      • 8. Honggang, Z., Kaili, Z., Song, Y.Z., et al: ‘Text extraction from natural scene image: a survey’, Neurocomputing, 2013, 122, (51), pp. 310323.
    9. 9)
      • 9. Kim, K.I., Jung, K., Jin, H.K.: ‘Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (12), pp. 16311639.
    10. 10)
      • 10. Ye, Q., Jiao, J., Huang, J., et al: ‘Text detection and restoration in natural scene images’, J. Vis. Commun. Image Represent., 2007, 18, (6), pp. 504513.
    11. 11)
      • 11. Yao, C., Bai, X., Liu, W., et al: ‘Detecting texts of arbitrary orientations in natural images’. IEEE Conf. Computer Vision and Pattern Recognition, Providence, USA, June 2012, p. 10831090.
    12. 12)
      • 12. Liu, Y.X., Ikenaga, T.: ‘A contour-based robust algorithm for text detection in color images’, IEICE Trans. Inf. Syst., 2006, 89, (3), p. 12211230.
    13. 13)
      • 13. Wang, K., Babenko, B., Belongie, S.: ‘End-to-end scene text recognition’. Proc. 2011 IEEE Int. Conf. Computer Vision, Barcelona, Spain, November 2011, p. 14571464.
    14. 14)
      • 14. Bai, J., Chen, Z., Feng, B., et al: ‘Image character recognition using deep convolutional neural network learned from different languages’. Proc. IEEE ICIP, Pairs, France, October 2014, p. 25602564.
    15. 15)
      • 15. Yan, C., Xie, H., Liu, S., et al: ‘Effective Uyghur language text detection in complex background images for traffic prompt identification’, IEEE Trans. Intell. Transp. Syst., 2017, PP, (99), pp. 110.
    16. 16)
      • 16. Girshick, R., Donahue, J., Darrell, T., et al: ‘Rich feature hierarchies for accurate object detection and semantic segmentation’. IEEE Conf. Computer Vision and Pattern Recognition, Columbus, USA, June 2014, pp. 580587.
    17. 17)
      • 17. Girshick, R.: ‘Fast R-CNN’. Int. Conf. Computer Vision, Santiago, Chile, December 2015.
    18. 18)
      • 18. Ren, S., He, K., Girshick, R., et al: ‘Faster R-CNN: towards real-time object detection with region proposal networks’. Int. Conf. Neural Information Processing Systems, Montreal, Canada, December 2015, pp. 9199.
    19. 19)
      • 19. He, K., Zhang, X., Ren, S., et al: ‘Spatial pyramid pooling in deep convolutional networks for visual recognition’. European Conf. Computer Vision (ECCV), Zurich, Switzerland, September 2014.
    20. 20)
      • 20. Tian, Z., Huang, W., He, T., et al: ‘Detecting text in natural image with connectionist text proposal network’. European Conf. Computer Vision (ECCV), Amsterdam, Netherlands, October 2016, pp. 5672.
    21. 21)
      • 21. Zhou, X., Yao, C., Wen, H., et al: ‘EAST: an efficient and accurate scene text detector’. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, USA, July 2017, pp. 26422651.
    22. 22)
      • 22. Huang, D.S.: ‘Systematic theory of neural networks for pattern recognition (in Chinese)’ (Publishing House of Electronic Industry of China, 1996).
    23. 23)
      • 23. Huang, D.S., Du, J.X.: ‘A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks’, IEEE Trans. Neural Netw., 2008, 19, (12), pp. 20992115.
    24. 24)
      • 24. Huang, D.S.: ‘Radial basis probabilistic neural networks: model and application’, Int. J. Pattern Recognit. Artif. Intell., 1999, 13, (07), pp. 10831101.
    25. 25)
      • 25. Huang, D.S., Jiang, W.: ‘A general CPL-AdS methodology for fixing dynamic parameters in dual environments’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2012, 42, (5), pp. 14891500.
    26. 26)
      • 26. Wang, X.F., Huang, D.S., Xu, H.: ‘An efficient local Chan-Vese model for image segmentation’ (Elsevier Science Inc., 2010).
    27. 27)
      • 27. Wang, X.F., Huang, D.S.: ‘A novel density-based clustering framework by using level set method’, IEEE Trans. Knowl. Data Eng., 2009, 21, (11), pp. 15151531.
    28. 28)
      • 28. Huang, D.S., Ip, H..S., Chi, Z.-R.: ‘A neural root finder of polynomials based on root moments’, Neural Comput., 2004, 16, (8), pp. 17211762.
    29. 29)
      • 29. Huang, D.S.: ‘A constructive approach for finding arbitrary roots of polynomials by neural networks’, IEEE Trans. Neural Netw., 2004, 15, (2), pp. 477491.
    30. 30)
      • 30. Huang, D.S., Ip, H.H.S., Law, K.C.K., et al: ‘Zeroing polynomials using modified constrained neural network approach’, IEEE Trans. Neural Netw., 2005, 16, (3), pp. 721732.
    31. 31)
      • 31. Zhu, L., Huang, D.S.: ‘A RayleighRitz style method for large-scale discriminant analysis’, Pattern Recognit., 2014, 47, (4), pp. 16981708.
    32. 32)
      • 32. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Int. Conf. Neural Information Processing Systems. Curran Associates Inc., Lake Tahoe, USA, December 2012, pp. 10971105.
    33. 33)
      • 33. Shannon, C.E.: ‘A mathematical theory of communication’, Bell Labs Tech. J., 1948, 27, (4), pp. 379423.
    34. 34)
      • 34. He, K., Zhang, X., Ren, S., et al: ‘Deep residual learning for image recognition’. 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, June 2016, pp. 770778.
    35. 35)
      • 35. Pan, S.J., Yang, Q.: ‘A survey on transfer learning’, IEEE Trans. Knowl. Data Eng., 2010, 22, (10), pp. 13451359.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.8318
Loading

Related content

content/journals/10.1049/joe.2018.8318
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address