Laser & Optoelectronics Progress, Volume. 56, Issue 21, 211504(2019)

Cross-Scale Local Stereo Matching Based on Edge Weighting

Deqiang Cheng1、*, Huandong Zhuang1、**, Wenjie Yu1, Chunmeng Bai1, and Xiaoshun Wen2
Author Affiliations
  • 1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
  • 2Wanbei Coal and Electricity Group Co., Ltd., Suzhou, Anhui 234000, China
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    Figures & Tables(10)
    Diagram of edge transform
    Teddy's local disparity maps. (a) Disparity map before weighting; (b) disparity map after weighting
    Disparity maps for different algorithms. (a) Ground truth maps; (b) CT-GF; (c) CGA-GF; (d) GA-MST; (e) GA-ST; (f) proposed algorithm
    Experimental results of proposed algorithm. (a) Left images; (b) ground truth maps; (c) disparity maps of proposed algorithm (without disparity refinement); (d) disparity maps of proposed algorithm (with disparity refinement); (e) mismatched maps of proposed algorithm (with disparity refinement)
    Experimental results of different parameter settings. (a) k1; (b) k2; (c) λn; (d) λs
    • Table 1. Experimental parameters

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      Table 1. Experimental parameters

      Parameterτcτgαk1k2λnλsTSλr
      Value0.027450.007840.110.90.70.20.12040.759
    • Table 2. Error matching rates before and after the improvement%

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      Table 2. Error matching rates before and after the improvement%

      AlgorithmTsukubaVenusTeddyConesAverage
      GF2.621.728.253.584.04
      S+GF2.301.096.993.233.40
      Proposedalgorithm2.120.916.593.063.17
    • Table 3. Comparison of run time of different algorithmss

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      Table 3. Comparison of run time of different algorithmss

      AlgorithmTsukubaVenusTeddyConesAverage
      S+GF0.811.565.085.433.22
      S+BF37.6070.84232.09231.78143.08
      Proposedalgorithm0.931.725.445.743.46
    • Table 4. Average matching error rates of different algorithms on non-occlusion regions%

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      Table 4. Average matching error rates of different algorithms on non-occlusion regions%

      Stereo pairsCT-GFCGA-GFGA-MSTGA-STProposed algorithm
      Tsukuba3.542.921.762.042.12
      Venus1.992.501.241.410.91
      Teddy8.568.235.736.226.59
      Cones4.873.754.424.763.06
      Aloe7.045.804.884.835.57
      Art12.159.7910.6910.388.56
      Baby13.313.318.214.493.56
      Baby24.033.7913.5415.122.76
      Baby34.975.285.593.963.85
      Books9.969.1310.6610.048.51
      Bowling16.187.4619.5618.888.75
      Bowling28.476.9010.1110.535.27
      Cloth11.961.160.630.691.29
      Cloth24.333.564.354.353.53
      Cloth32.802.052.902.962.28
      Cloth42.381.951.881.841.68
      Dolls7.275.715.895.494.80
      Flowerpots9.6712.9216.7912.508.54
      Lampshade110.6411.639.819.137.10
      Lampshade210.5215.9712.089.8912.46
      Laundry18.4518.5011.9211.9012.03
      Midd133.4537.6124.4322.0132.90
      Midd232.9635.5320.5718.9026.61
      Moebius10.9210.757.577.308.11
      Monopoly18.9923.4321.0320.7521.68
      Plastic22.2329.3945.0237.3925.74
      Reindeer8.159.369.797.836.05
      Rocks14.033.943.353.063.00
      Rocks22.742.202.281.991.53
      Wood14.934.9910.185.463.66
      Wood22.952.963.174.841.78
      Average matching error rate9.189.7610.009.067.88
      Average rank3.613.423.392.681.93
    • Table 5. Percentage of mismatching pixels in different regions for different algorithms%

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      Table 5. Percentage of mismatching pixels in different regions for different algorithms%

      AlgorithmTsukubaVenusTeddyConesAverage
      Non-occAllDiscNon-occAllDiscNon-occAllDiscNon-occAllDisc
      GC+occ[5]1.192.016.241.642.196.7511.217.419.85.3612.413.08.26
      Adapt Weight[10]1.381.856.900.711.196.137.8813.318.63.979.798.266.67
      TiwceGF[17]1.822.997.350.341.533.456.9314.7416.083.212.2911.456.84
      ASSW[21]1.812.177.850.320.513.737.0212.517.43.218.408.996.16
      iFBS[22]1.782.107.570.310.502.177.9412.817.13.078.738.466.05
      SDDS[23]3.313.6210.40.390.762.857.6513.019.43.9910.010.87.19
      VSW[24]1.621.886.980.470.813.408.6713.318.03.378.858.126.29
      Proposed algorithm2.022.298.660.260.482.886.1111.415.772.948.808.075.81
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    Deqiang Cheng, Huandong Zhuang, Wenjie Yu, Chunmeng Bai, Xiaoshun Wen. Cross-Scale Local Stereo Matching Based on Edge Weighting[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211504

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    Paper Information

    Category: Machine Vision

    Received: Mar. 20, 2019

    Accepted: Apr. 30, 2019

    Published Online: Nov. 2, 2019

    The Author Email: Deqiang Cheng (chengdq@cumt.edu.cn), Huandong Zhuang (hdzhuang@cumt.edu.cn)

    DOI:10.3788/LOP56.211504

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