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All issues Volume 176 (2018) MATEC Web Conf., 176 (2018) 01035 References
Open Access
Issue
MATEC Web Conf.
Volume 176, 2018
2018 6th International Forum on Industrial Design (IFID 2018)
Article Number 01035
Number of page(s) 6
Section Intelligent Design and Computer Technology
DOI https://doi.org/10.1051/matecconf/201817601035
Published online 02 July 2018
  1. Smith, J., 1998. The book, The publishing company. London, 2nd edition. [Google Scholar]
  2. Xuelong Li, Zhengkai Liu. An Efficient Semantic Image Classification Method [J]. Journal of Circuits and Systems, 2002, 7(2): 22-25. [Google Scholar]
  3. Hao Zhang. Extraction and Analysis of Semantic Image Features[D]. Shanghai Jiao Tong University, 2006. [Google Scholar]
  4. Yao Y. An Outline of a Theory of Three-Way Decisions[M]// Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg, 2012: 1-17. [Google Scholar]
  5. Dun Liu. The three-way decision and granular computing[M]. China Science Pubic, 2013. [Google Scholar]
  6. Liang Decui. The research of decision-making method based on decision-theoretic rough set under the fuzzy environment[D]. Southwest Jiaotong University, 2014. [Google Scholar]
  7. Li Deyi, Liu Changyu, Du Yi, et al. Artificial Intelligence with Uncertainty[J]. Journal of Software, 2004, 15(11): 1583-1594. [Google Scholar]
  8. Wang X, Yang M, Zhu S, et al. Regionlets for Generic Object Detection[C]// IEEE International Conference on Computer Vision. IEEE, 2014: 17-24. [Google Scholar]
  9. Long C, Wang X, Hua G, et al. Accurate Object Detection with Location Relaxation and Regionlets Re-localization[C]// Asian Conference on Computer Vision. Springer, Cham, 2014: 260-275. [Google Scholar]
  10. Shao Xiaoyan, Li Yan, Li Lihong. Transformation of no-commitment decision of three-way decision based on the maximization of income[J]. Microcomputer its Applications, 2017, 36(8): 79-82. [Google Scholar]
  11. Yin Ye, Ke Deying, Liu Chuanyong. Throry of trinary decision and its application[J]. Journal of Shanghai Normal University (Natural Sciences), 2015, 44(1): 95-104. [Google Scholar]
  12. Liu Baoxiang, Li Yan, Sun Jie. Three-way decisions and related theoretical research[J]. Microcomputer its Applications, 2014(12): 1-3. [Google Scholar]
  13. Carvalho E F, Engel P M. Convolutional Sparse Feature Descriptor for Object Recognition in CIFAR-10[C]// Brazilian Conference on Intelligent Systems. IEEE Computer Society, 2013: 131-135. [Google Scholar]
  14. Masnadishirazi H, Vasconcelos N. Cost-sensitive boosting.[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2010, 33(2): 294-309. [CrossRef] [Google Scholar]
  15. Wu J, Brubaker S C, Mullin M D, et al. Fast Asymmetric Learning for Cascade Face Detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2008, 30(3): 369. [CrossRef] [Google Scholar]
  16. Viola P, Jones M. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154. [Google Scholar]
  17. Paisitkriangkrai S, Shen C, Hengel A V D. Asymmetric Pruning for Learning Cascade Detectors[J]. IEEE Transactions on Multimedia, 2014, 16(5): 1254-1267. [CrossRef] [Google Scholar]
  18. Hu Q, Paisitkriangkrai S, Shen C, et al. Fast Detection of Multiple Objects in Traffic Scenes with a Common Detection Framework[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4): 1002-1014. [CrossRef] [Google Scholar]
  19. Behley J, Steinhage V, Cremers A B. Laser-based segment classification using a mixture of bag-ofwords[C]// Ieee/rsj International Conference on Intelligent Robots and Systems. IEEE, 2013: 4195- 4200. [Google Scholar]
  20. Forsyth D. Object Detection with Discriminatively Trained Part-Based Models[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 47(2): 6-7. [Google Scholar]
  21. Yebes J J, Bergasa L M, Arroyo R, et al. Supervised learning and evaluation of KITTI's cars detector with DPM[C]// Intelligent Vehicles Symposium Proceedings. IEEE, 2014: 768-773. [Google Scholar]
  22. Pepikj B, Stark M, Gehler P, et al. Occlusion Patterns for Object Class Detection[C]// Computer Vision and Pattern Recognition. IEEE, 2013: 3286- 3293. [Google Scholar]
  23. Li B, Wu T, Zhu S C. Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model[M]// Computer Vision – ECCV 2014. Springer International Publishing, 2014: 652-667. [Google Scholar]
  24. Ohn-Bar E, Trivedi M M. Fast and Robust Object Detection Using Visual Subcategories[C]// Computer Vision and Pattern Recognition Workshops. IEEE, 2014: 179-184. [Google Scholar]

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