• Laser & Optoelectronics Progress
  • Vol. 54, Issue 12, 121504 (2017)
Fan Qiang* and Zhang Shanxin
Author Affiliations
  • [in Chinese]
  • show less
    DOI: 10.3788/lop54.121504 Cite this Article Set citation alerts
    Fan Qiang, Zhang Shanxin. Object Shape Classification Based on Improved Bayesian Program Learning[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121504 Copy Citation Text show less
    References

    [1] Shen W, Wang X G, Yao C, et al. Shape recognition by combining contour and skeleton into a mid-level representation[C]. Chinese Conference on Pattern Recognition, 2014: 391-400.

    [2] Bai X, Liu W Y, Tu Z W. Integrating contour and skeleton for shape classification[C]. IEEE International Conference on Computer Vision Workshops, 2009: 360-367.

    [3] Wang J W, Bai X, You X G, et al. Shape matching and classification using height functions[J]. Pattern Recognition Letters, 2012, 33(2): 134-143.

    [4] Ling H B, Jacobs D W. Shape classification using the inner-distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 286-299.

    [5] Felzenszwalb P F, Schwartz J D. Hierarchical matching of deformable shapes[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2007: 1-8.

    [6] Wang X G, Feng B, Bai X, et al. Bag of contour fragments for robust shape classification[J]. Pattern Recognition, 2014, 47(6): 2116-2125.

    [7] Ramesh B, Xiang C, Lee T H. Shape classification using invariant features and contextual information in the bag-of-words model[J]. Pattern Recognition, 2015, 48(3): 894-906.

    [8] Yang Xiaojun, Yang Xingwei, Zeng Luan, et al. Shape classification using contour critical point sets[J]. Journal of Nanjing University (Natural science), 2010, 46(1): 47-55.

    [9] Giang N T, Tao N Q, Dung N D, et al. Skeleton based shape matching using reweighted random walks[C]. 9th International Conference on Information, Communications & Signal Processing, 2013: 1-5.

    [10] Shen W, Bai X, Yang X W, et al. Skeleton pruning as trade-off between skeleton simplicity and reconstruction error[J]. Science China Information Sciences, 2013, 56(4): 1-14.

    [11] Shen W, Jiang Y, Gao W J, et al. Shape recognition by bag of skeleton-associated contour parts[J]. Pattern Recognition Letters, 2016, 83(P3): 321-329.

    [12] Bicego M, Lovato P. A bioinformatics approach to 2D shape classification[J]. Computer Vision and Image Understanding, 2016, 145(C): 59-69.

    [13] Lake B M, Salakhutdinov R, Tenenbaum J B. Human-level concept learning through probabilistic program induction[J]. Science, 2015, 350(6266): 1332-1338.

    [14] Guo Pengyu, Su Ang, Zhang Hongliang, et al. Online mixed of random naive Bayes tracker combined texture with shape features[J]. Acta Optica Sinica, 2015, 35(3): 0315002.

    [15] Sun Tao, Wang Canjin, Wang Rui, et al. Contour bag of features applied in laser active lighting recognition system[J]. Chinese J Lasers, 2015, 42(1): 0109002.

    [16] Li Chengfei, Chen Xinhua. Vehicle type recognition based on combining local binary pattern and Hu matrix feature[J]. Laser & Optoelectronics Progress, 2016, 53(10): 101503.

    CLP Journals

    [1] Li Changyong, Wu Jinqiang, Fang Aiqing. A Multi-Information-Based Fatigue State Recognition Method[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101503

    [2] Shanxin Zhang, Qiang Fan, Zhiping Zhou. Object Shape Classification Based on Bayesian Optimized Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061011

    Fan Qiang, Zhang Shanxin. Object Shape Classification Based on Improved Bayesian Program Learning[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121504
    Download Citation