Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Laser & Optoelectronics Progress, Volume. 57, Issue 8, 081504(2020)

Stereo Matching by Improved Window Characteristics and Differential Operators

Xinchun Li1, Xinyong Yin1、*, and Sen Lin1,2,3
Author Affiliations
  • 1School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
  • 2State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 3Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
    Equations (0)
    References (20)
    Cited By (1)
    Tools
    Paper Information
    Topics
    Figures & Tables(14)
    Direct relevant neighborhood of matching points

    Fig. 1. Direct relevant neighborhood of matching points

    Download full size
    Window shape and weight allocation

    Fig. 2. Window shape and weight allocation

    Download full size
    Main flow chart of matching in this paper

    Fig. 3. Main flow chart of matching in this paper

    Download full size
    Sample datasets diagrams (Jadeplant). (a) Left image; (b) right image; (c) ground truth; (d) mask

    Fig. 4. Sample datasets diagrams (Jadeplant). (a) Left image; (b) right image; (c) ground truth; (d) mask

    Download full size
    Mismatching rate of different window shapes

    Fig. 5. Mismatching rate of different window shapes

    Download full size
    Different types of matching windows

    Fig. 6. Different types of matching windows

    Download full size
    Example of the effect improvement of proposed algorithm and the original method. (a) Left images; (b) ground truth images; (c) ELAS; (d) proposed algorithm

    Fig. 7. Example of the effect improvement of proposed algorithm and the original method. (a) Left images; (b) ground truth images; (c) ELAS; (d) proposed algorithm

    Download full size
    bad index comparison

    Fig. 8. bad index comparison

    Download full size
    avgErr index comparison chart

    Fig. 9. avgErr index comparison chart

    Download full size
    Sample graphs of effectiveness improvement. (a) Left images; (b) ground truth; (c) GA-Net; (d) GwcNet; (e) Census; (f) SGM; (g) proposed algorithm

    Fig. 10. Sample graphs of effectiveness improvement. (a) Left images; (b) ground truth; (c) GA-Net; (d) GwcNet; (e) Census; (f) SGM; (g) proposed algorithm

    Download full size
    Self-collecting images. (a) Left images; (b) GA-Net; (c) GwcNet; (d) Census; (e) SGM; (f) proposed algorithm

    Fig. 11. Self-collecting images. (a) Left images; (b) GA-Net; (c) GwcNet; (d) Census; (e) SGM; (f) proposed algorithm

    Download full size

    • Table 1. Performance comparison of Gaussian weight window under two indices

      View table

      Table 1. Performance comparison of Gaussian weight window under two indices

      Data nameEbad /%Eavg /%Data name
      ELASProposedELASProposed
      Adirondack12.07011.8900.8800.877Adirondack
      ArtL19.80019.2701.7601.630ArtL
      Jadeplant34.68034.6601.1701.170MotorcycleE
      MotorcycleE13.45013.2102.2102.080PianoL
      PianoL37.52036.5301.9601.930Pipes
      Pipes15.30014.7601.9001.800layroom
      Playroom26.68026.0404.5504.270Playtable
      Playtable26.38026.2900.9300.920PlaytableP
      PlaytableP14.84014.7400.8900.890Recycle
      Recycle17.85016.8500.9100.910Teddy
      Vintage33.69033.6602.4302.430Vintage

    • Table 2. Performance comparison of Scharr filter under two indices

      View table

      Table 2. Performance comparison of Scharr filter under two indices

      Data nameEbad /%Eavg /%Data name
      ELASProposedELASProposed
      ArtL19.8019.730.880.86Adirondack
      Jadeplant34.6829.198.856.33Jadeplant
      MotorcycleE13.4513.091.171.13MotorcycleE
      Piano22.0421.922.212.22PianoL
      PianoL37.5237.501.961.97Pipes
      Pipes15.3015.190.930.92PlaytableP
      Playroom26.6825.630.910.89Teddy
      PlaytableP14.8414.79---
      Recycle17.8517.24---

    • Table 3. Performance comparison of proposed algorithm under two indices

      View table

      Table 3. Performance comparison of proposed algorithm under two indices

      Data nameEbad /%Eavg /%Data name
      ELASProposedELASProposed
      Adirondack12.0711.810.880.83Adirondack
      ArtL19.8019.551.761.74ArtL
      Jadeplant34.6827.858.955.99Jadeplant
      MotorcycleE13.4513.301.171.15Motorcycle
      Piano22.0421.851.171.16MotorcycleE
      PianoL37.5236.032.211.94PianoL
      Pipes15.3015.301.961.94Pipes
      Playroom26.6825.630.930.92PlaytableP
      Recycle17.8517.121.971.92Shelves
      Teddy11.4210.880.910.83Teddy
      Vintage33.6932.332.432.39Vintage
    Tools

    Get Citation

    Copy Citation Text

    Xinchun Li, Xinyong Yin, Sen Lin. Stereo Matching by Improved Window Characteristics and Differential Operators[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081504

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Machine Vision

    Received: Aug. 29, 2019

    Accepted: Sep. 24, 2019

    Published Online: Apr. 3, 2020

    The Author Email: Xinyong Yin (xin-yong.yin@outlook.com)

    DOI:10.3788/LOP57.081504

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology