• Infrared and Laser Engineering
  • Vol. 50, Issue 8, 20200407 (2021)
Hongyu Chen1、2、3、4、5, Haibo Luo1、2、4、5, Bin Hui1、2、4、5, and Zheng Chang1、2、4、5
Author Affiliations
  • 1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
  • 2Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4Key Laboratory of Opto-electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
  • 5The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China
  • show less
    DOI: 10.3788/IRLA20200407 Cite this Article
    Hongyu Chen, Haibo Luo, Bin Hui, Zheng Chang. Automatic parts selection method based on multi-feature fusion[J]. Infrared and Laser Engineering, 2021, 50(8): 20200407 Copy Citation Text show less
    References

    [1] H B Luo, L Y Xu, B Hui, et al. Status and prospect of target tracking based on deep learning. Infrared and Laser Engineering, 46, 0502002(2017).

    [2] Y F Tang, Z J Wang, Z X Zhang. Registration of sand dune images using an improved SIFT and SURF algorithm. Journal of Tsinghua University (Science and Technology), 61, 161-169(2021).

    [3] A Rodriguez, D B Ehlenberger, P R Hof, et al. Three-dimensional neuron tracing by voxel scooping. Journal of Neuroscience Methods, 184, 169-175(2009).

    [4] Fan H, Xiang J. Robust visual tracking via localglobal crelation filter[C]AAAI Conference on Artificial Intelligence, 2017.

    [5] X D Chen, J Sheng, J Yang, et al. Ultrasound image segmentation based on a multi-parameter Gabor filter and multiscale local level set method. Chinese Optics, 13, 1075-1084(2020).

    [6] Wang Q, Zhang L, Bertito L, et al. Fast online object tracking segmentation: A unifying approach[C]IEEE Conference on Computer Vision Pattern Recognition, 2018.

    [7] J K Ma, H B Luo, Chang, Z, et al. Visual tracking algorithm based on deformable parts model. Infrared and Laser Engineering, 46, 0928001(2017).

    [8] H B Luo, Z Chang, X R Yu, et al. Automatic suitable-matching area selection method based on multi-feature fusion. Infrared and Laser Engineering, 40, 2037-2041(2011).

    [9] Hou X D, Zhang L P. Saliency detection: A spectral residual approach[C]Computer Vision Pattern Recognition, 2007: 18.

    [10] H Y Chen, S Xu, K Liu, et al. Surface defect detection of steel strip based on spectral residual visual saliency. Optics and Precision Engineering, 24, 2572-2580(2016).

    [11] Wu Y, Lim J, Yang M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2015, 37(9): 18341848.

    [12] J F Henriques, R Caseiro, P Martins, et al. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 583-596(2015).

    [13] Z Kalal, K Mikolajczyk, J Matas. Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1409-1422(2012).

    [14] Bao C L, Wu Y, Ling H B, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]IEEE Conference on Computer Vision Pattern Recognition, 2012: 18301837.

    [15] F L Chen, Q H Ding, H B Luo, et al. Anti-occlusion real time target tracking algorithm employing spatio-temporal context. Infrared and Laser Engineering, 50, 20200105(2021).

    Hongyu Chen, Haibo Luo, Bin Hui, Zheng Chang. Automatic parts selection method based on multi-feature fusion[J]. Infrared and Laser Engineering, 2021, 50(8): 20200407
    Download Citation