Open Access
MATEC Web of Conferences
Volume 59, 2016
2016 International Conference on Frontiers of Sensors Technologies (ICFST 2016)
Article Number 08001
Number of page(s) 5
Section Image processing
Published online 24 May 2016
  1. L. Zhou, Y. Zhang, Y. Sun, T. Liang, and S. Lu, “Development and application of equipment inspection robot for smart substation,” Automation of Electric Power Systems, 35, 85-88, (2011). [Google Scholar]
  2. H. Zhang, W. Wang, L. J. Xu, H. Qin, and M. Liu, “Application of image recognition technology in electrical equipment on-line monitoring,” Power System Protection and Control, 38 (2010). [Google Scholar]
  3. L. Li, P. Li, M. Yang, B. Zheng, and B. H. Wang, “Research on abnormal appearance detection approach of electric power equipment,” Optics and Optoelectronic Technology, 8 (2010). [Google Scholar]
  4. M. Brown, and D. G. Lowe, “Automatic panoramic image stitching using invariant features,” International Journal of Computer Vision, 74(2007). [Google Scholar]
  5. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: an efficient alternative to SIFT or SURF,” Proc.13th Computer Vision Conf, 2564-2571, (2011). [Google Scholar]
  6. B. Ko and J. Nam, “Object-of-interest image segmentation based on human attention and semantic region clustering,” Journal of the Optical Society of America, 23 (2006). [Google Scholar]
  7. T. Judd, K. Ehinger, F. Durand, and A. Torralba, “Learning to Predict Where Humans Look,” Proc.12th Computer Vision Conf, Kyoto (2009). [Google Scholar]
  8. T. Chen, M. M. Cheng, P. Tan, A. Shamir, and S.M. Hu, “Sketch2photo: Internet image montage,” ACM Trans, 28 (2009). [Google Scholar]
  9. R. Achanta, F. Estrada, P. Wils, and S. Sabine, “Salient region detection and segmentation,” Proc. 6th Computer Vision Systems Conf., 66-75, (2008). [Google Scholar]
  10. B. Herbert, A. Ess, T. Tinne, and G. L. Van, “SURF: speeded up robust features,” Computer Vision and Image Understanding, 110 (2008). [Google Scholar]
  11. M. A. Fischer and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, 24 (1981). [Google Scholar]
  12. L. Bruzzone, and D. F. Prieto, “An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images,” IEEE Trans. On Image Processing, 11 (2002). [Google Scholar]
  13. A. Huertas and R. Nevatia, “Detecting changes in aerial views of man-made structures,” Image and Vision Computing, 18 (2000). [Google Scholar]
  14. M. K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. on Information Theory, 8(1962). [Google Scholar]
  15. J. Yang, Y. Shi, J. Yang, and L. Jiang, “A novel finger-vein recognition method with feature combination,” Proc. IEEE International Conference on Image Processing, 2709-2712, (2009). [Google Scholar]
  16. N. Dalal, and B. Triggs, “Histograms of oriented gradients for human detection,” Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 886-893, (2005). [Google Scholar]
  17. X. Zhang and F. Fang, “Multivariate statistical introduction,” Sciences Press, 16 (1999). [Google Scholar]
  18. D. R. Hardoon, S. Szedmak, O. Szedmak, and J. Shawe-Taylor, “Canonical correlation analysis: an overview with application to learning methods,” Neural Computation, 16(2004). [Google Scholar]

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