• Acta Optica Sinica
  • Vol. 39, Issue 6, 0610004 (2019)
Yang Wang1、2, Liqiang Zhu1、2、*, Zujun Yu1、2, and Baoqing Guo1、2
Author Affiliations
  • 1 School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
  • 2 Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology, Beijing Jiaotong University, Beijing 100044, China
  • show less
    DOI: 10.3788/AOS201939.0610004 Cite this Article Set citation alerts
    Yang Wang, Liqiang Zhu, Zujun Yu, Baoqing Guo. Segmentation and Recognition Algorithm for High-Speed Railway Scene[J]. Acta Optica Sinica, 2019, 39(6): 0610004 Copy Citation Text show less
    References

    [1] He F L, Guo Y C, Gao C. Improved PCNN method for human target infrared image segmentation under complex environments[J]. Acta Optica Sinica, 37, 0215003(2017).

         He F L, Guo Y C, Gao C. Improved PCNN method for human target infrared image segmentation under complex environments[J]. Acta Optica Sinica, 37, 0215003(2017).

    [2] Wu C Y, Yi B S, Zhang Y G et al. Retinal vessel image segmentation based on improved convolutional neural network[J]. Acta Optica Sinica, 38, 1111004(2018).

         Wu C Y, Yi B S, Zhang Y G et al. Retinal vessel image segmentation based on improved convolutional neural network[J]. Acta Optica Sinica, 38, 1111004(2018).

    [3] Guo B Q, Yang L X, Shi H M et al. High-speed railway clearance intrusion detection algorithm with fast background subtraction[J]. Chinese Journal of Scientific Instrument, 37, 1371-1378(2016).

         Guo B Q, Yang L X, Shi H M et al. High-speed railway clearance intrusion detection algorithm with fast background subtraction[J]. Chinese Journal of Scientific Instrument, 37, 1371-1378(2016).

    [4] Wang Y, Yu Z J, Zhu L Q et al. High-speed railway clearance surveillance system based on convolutional neural networks[J]. Proceedings of SPIE, 10033, 100335S(2016). http://proceedings.spiedigitallibrary.org/article.aspx?articleid=2548016

         Wang Y, Yu Z J, Zhu L Q et al. High-speed railway clearance surveillance system based on convolutional neural networks[J]. Proceedings of SPIE, 10033, 100335S(2016). http://proceedings.spiedigitallibrary.org/article.aspx?articleid=2548016

    [5] Achanta R, Shaji A, Smith K et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012). http://dl.acm.org/citation.cfm?id=2377556

         Achanta R, Shaji A, Smith K et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012). http://dl.acm.org/citation.cfm?id=2377556

    [6] Chen H Y, Qie L Z, Yang D D et al. Visual background extraction algorithm based on superpixel information feedback[J]. Acta Optica Sinica, 37, 0715001(2017).

         Chen H Y, Qie L Z, Yang D D et al. Visual background extraction algorithm based on superpixel information feedback[J]. Acta Optica Sinica, 37, 0715001(2017).

    [7] Liu Y C, Chen Y P, Zhang S S et al. Traffic sign recognition based on pyramid histogram fusion descriptor and HIK-SVM[J]. Journal of Transportation Systems Engineering and Information Technology, 17, 220-226(2017).

         Liu Y C, Chen Y P, Zhang S S et al. Traffic sign recognition based on pyramid histogram fusion descriptor and HIK-SVM[J]. Journal of Transportation Systems Engineering and Information Technology, 17, 220-226(2017).

    [8] Fang Z P, Duan J M, Zheng B G. Traffic signs recognition and tracking based on feature color and SNCC algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 14, 47-52(2014).

         Fang Z P, Duan J M, Zheng B G. Traffic signs recognition and tracking based on feature color and SNCC algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 14, 47-52(2014).

    [9] Liu K P, Ying Z L, Zhai Y K et al. SAR image target recognition based on unsupervised K-means feature and data augmentation[J]. Journal of Signal Processing, 33, 452-458(2017).

         Liu K P, Ying Z L, Zhai Y K et al. SAR image target recognition based on unsupervised K-means feature and data augmentation[J]. Journal of Signal Processing, 33, 452-458(2017).

    [10] Zhang X D, Fan J L, Xu J et al. Image super-resolution algorithm via K-means clustering and support vector data description[J]. Journal of Image and Graphics, 21, 135-144(2016).

         Zhang X D, Fan J L, Xu J et al. Image super-resolution algorithm via K-means clustering and support vector data description[J]. Journal of Image and Graphics, 21, 135-144(2016).

    [11] Ma G Q, Tian Y C, Li X L. Application of K-means clustering algorithm in colour image segmentation of grouper in seawater background[J]. Computer Applications and Software, 33, 192-195(2016).

         Ma G Q, Tian Y C, Li X L. Application of K-means clustering algorithm in colour image segmentation of grouper in seawater background[J]. Computer Applications and Software, 33, 192-195(2016).

    [12] Arbeláez P, Pont-Tuset J, Barron J T, Marques F et al. Multiscale combinatorial grouping. [C]∥2004 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 328-335(2014).

         Arbeláez P, Pont-Tuset J, Barron J T, Marques F et al. Multiscale combinatorial grouping. [C]∥2004 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 328-335(2014).

    [13] Arbeláez P, Maire M, Fowlkes C et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 898-916(2011). http://www.mendeley.com/research/community-detection-hierarchical-image-segmentation-4/

         Arbeláez P, Maire M, Fowlkes C et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 898-916(2011). http://www.mendeley.com/research/community-detection-hierarchical-image-segmentation-4/

    [14] Arbeláez P. Boundary extraction in natural images using ultrametric contour maps. [C]∥2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), June 17-22, 2006, New York, USA. New York: IEEE, 182(2006).

         Arbeláez P. Boundary extraction in natural images using ultrametric contour maps. [C]∥2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), June 17-22, 2006, New York, USA. New York: IEEE, 182(2006).

    [15] Farabet C, Couprie C, Najman L et al. Learning hierarchical features for scene labeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1915-1929(2013). http://www.ncbi.nlm.nih.gov/pubmed/23787344/

         Farabet C, Couprie C, Najman L et al. Learning hierarchical features for scene labeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1915-1929(2013). http://www.ncbi.nlm.nih.gov/pubmed/23787344/

    [16] Couprie C, Farabet C, Najman L et al. -03-14)[2019-01-25]. https:∥arxiv., org/abs/1301, 3572(2013).

         Couprie C, Farabet C, Najman L et al. -03-14)[2019-01-25]. https:∥arxiv., org/abs/1301, 3572(2013).

    [17] Gupta S, Girshick R, Arbeláez P et al. Learning rich features from RGB-D images for object detection and segmentation[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Cham: Springer, 8695, 345-360(2014).

         Gupta S, Girshick R, Arbeláez P et al. Learning rich features from RGB-D images for object detection and segmentation[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Cham: Springer, 8695, 345-360(2014).

    [18] Petrelli A. Pau D, di Stefano L. Analysis of compact features for RGB-D visual search[M]. ∥Murino V, Puppo E. Image Analysis and Processing-ICIAP 2015. Cham: Springer, 9280, 14-24(2015).

         Petrelli A. Pau D, di Stefano L. Analysis of compact features for RGB-D visual search[M]. ∥Murino V, Puppo E. Image Analysis and Processing-ICIAP 2015. Cham: Springer, 9280, 14-24(2015).

    [19] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017). http://ieeexplore.ieee.org/document/7478072/

         Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017). http://ieeexplore.ieee.org/document/7478072/

    [20] Zheng T Y, Tang C, Lei Z K. Multi-scale retinal vessel segmentation based on fully convolutional neural network[J]. Acta Optica Sinica, 39, 0211002(2019).

         Zheng T Y, Tang C, Lei Z K. Multi-scale retinal vessel segmentation based on fully convolutional neural network[J]. Acta Optica Sinica, 39, 0211002(2019).

    Yang Wang, Liqiang Zhu, Zujun Yu, Baoqing Guo. Segmentation and Recognition Algorithm for High-Speed Railway Scene[J]. Acta Optica Sinica, 2019, 39(6): 0610004
    Download Citation