• Laser & Optoelectronics Progress
  • Vol. 57, Issue 4, 041009 (2020)
Kaixuan Wang, Zhuorong Li, Xiaobin Wang, Shengdong Yan, and Yunqi Tang*
Author Affiliations
  • School of Criminal Investigation and Forensic Science, People's Public Security University of China, Beijing 100038, China
  • show less
    DOI: 10.3788/LOP57.041009 Cite this Article Set citation alerts
    Kaixuan Wang, Zhuorong Li, Xiaobin Wang, Shengdong Yan, Yunqi Tang. Automated Classification Method for Crime Scene Sketches[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041009 Copy Citation Text show less
    References

    [1] Wang L J[J]. Explore the new mechanism of scene investigation of "one head four must" Legal and Economy, 2016, 172-174.

    [2] Zhou P, Sheng S Z. The influence of "one head four must" on crime scene investigation[J]. Industrial & Science Tribune, 17, 37-38(2018).

    [3] Yang Z Z, Kuang N, Fan L et al. Review of image classification algorithms based on convolutional neural networks[J]. Journal of Signal Processing, 34, 1474-1489(2018).

    [4] Raghavendra U, Fujita H, Bhandary S V et al. Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images[J]. Information Sciences, 441, 41-49(2018).

    [5] Acharya U R, Oh S L, Hagiwara Y et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals[J]. Computers in Biology and Medicine, 100, 270-278(2018).

    [6] Yang M J, Tang Y Q, Jiang X J. Novel shoe type recognition method based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 191505(2019).

    [7] Wang D C, Chen X N, Zhao F et al. Vehicle detection algorithm based on convolutional neural network and RGB-D images[J]. Laser & Optoelectronics Progress, 56, 181003(2019).

    [8] Li Z R, Wang K X, He X L et al. Research on heel strike events detection algorithm based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 211503(2019).

    [9] LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. [C]∥Proceedings of 2010 IEEE International Symposium on Circuits and Systems, May 30-June 2, 2010, Paris, France. New York: IEEE, 253-256(2010).

    [10] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Advances in Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada, United States. Canada: NIPS, 1097-1105(2012).

    [11] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 15523970(2015).

    [12] Simonyan K. -04-10)[2019-06-11]. https:∥arxiv., org/abs/1409, 1556(2015).

    [13] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

    [14] Hao H K[M]. Crime scene investigation(2008).

    [15] Gao G D, Wang D Z, Zhu X[M]. Encyclopedia offorensicscience in China: crime scene investigation technology(2003).

    [16] Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines. [C]∥Proceedings of the 27th international conference on machine learning (ICML-10), June 21-24, 2010, Haifa, Israel. [S.l.: s.n.], 807-814(2010).

    [17] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. [C]∥Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, March 2010, Chia Laguna Resort, Sardinia, Italy. [S.l.: s.n.], 249-256(2010).

    [18] Lin M, Chen Q. -03-04)[2019-06-11]. https:∥arxiv., org/abs/1312, 4400(2014).

    [19] Ioffe S. -03-02)[2019-06-11]. https:∥arxiv., org/abs/1502, 03167(2015).

    [20] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2818-2826(2016).

    [21] Szegedy C, Ioffe S, Vanhoucke V et al. Inception-v4, inception-ResNet and the impact of residual connections on learning. [C]∥Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA. USA: AAAI, 4278-4824(2017).

    [22] Jia Y, Shelhamer E, Donahue J et al. Caffe: convolutional architecture for fast feature embedding. [C]∥Proceedings of the 22nd ACM International Conference on Multimedia, November 3-7, 2014, Orlando, Florida, USA. New York: ACM, 675-678(2014).

    Kaixuan Wang, Zhuorong Li, Xiaobin Wang, Shengdong Yan, Yunqi Tang. Automated Classification Method for Crime Scene Sketches[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041009
    Download Citation