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Laser & Optoelectronics Progress, Volume. 57, Issue 4, 041017(2020)

Fundamental Matrix Estimation Based on Multiple Kernel Learning-Density Peak Clustering

Jianfeng Wang1、*, Hongwei Wang1,2、**, and Xueqin Yan1、***
Author Affiliations
  • 1School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China
  • 2School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
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    References (22)
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    Figures & Tables(10)
    Epipolar geometry

    Fig. 1. Epipolar geometry

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    Experimental results of basic density peak algorithm(dc=1.8). (a) Decision graph; (b) clustering results

    Fig. 2. Experimental results of basic density peak algorithm(dc=1.8). (a) Decision graph; (b) clustering results

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    Experimental results of multi-kernel learning-density peak algorithm. (a) Decision graph; (b) clustering results

    Fig. 3. Experimental results of multi-kernel learning-density peak algorithm. (a) Decision graph; (b) clustering results

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    Distribution map of γ

    Fig. 4. Distribution map of γ

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    Raster division diagram

    Fig. 5. Raster division diagram

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    Flow chart of basic matrix estimation

    Fig. 6. Flow chart of basic matrix estimation

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    Average epipolar distance of four methods under different Gaussian variance noise

    Fig. 7. Average epipolar distance of four methods under different Gaussian variance noise

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    Average epipolar distance of four methods at different external point rates

    Fig. 8. Average epipolar distance of four methods at different external point rates

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    Experimental images. (a) Graffiti; (b) tree; (c) UBC; (d) bike; (e) fruit; (f) INRIA;(g) building; (h) snow-tree

    Fig. 9. Experimental images. (a) Graffiti; (b) tree; (c) UBC; (d) bike; (e) fruit; (f) INRIA;(g) building; (h) snow-tree

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    • Table 1. Comparison of four algorithms performance

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      Table 1. Comparison of four algorithms performance

      PerformanceindicatorAlgorithmGraffitiTreeUBCBikeFruitINRIABuildingSnow-tree
      Total numberof matchedpointsSURF84722311216700289345576264
      Number of matchedpoints forestimatingfundamental matrixLMedS4241116608350146173288132
      R-RANSAC18567027157313711535
      LO-RANSAC712311722111125020
      Proposed1322281783920254926
      AverageresidualerrorLMedS8.66211.37382.31533.63932.61011.80342.66501.6481
      R-RANSAC7.27441.04931.21012.24661.04661.16323.96131.2221
      LO-RANSAC1.38970.30080.82000.49270.28260.45190.10521.3596
      Proposed1.05130.25380.70170.38590.21830.39720.08441.1466
      Averageepipolardistance /pixelLMedS0.38090.24660.35860.36510.29810.31520.39850.6759
      R-RANSAC0.15110.13620.14610.17120.10260.14810.13240.1192
      LO-RANSAC0.05880.09610.07810.09020.07180.06600.07520.0956
      Proposed0.04140.08300.06910.07820.06730.05920.06440.0868
      Computationaltime /sLMedS0.14810.21760.28170.17740.10530.12280.17360.1378
      R-RANSAC0.16590.23510.41600.14050.08120.13690.16930.1596
      LO-RANSAC0.13020.17700.09060.12350.04430.10430.13960.1206
      Proposed0.14930.20600.09750.10290.04270.08710.09060.1415
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    Jianfeng Wang, Hongwei Wang, Xueqin Yan. Fundamental Matrix Estimation Based on Multiple Kernel Learning-Density Peak Clustering[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041017

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    Paper Information

    Category: Image Processing

    Received: Jul. 4, 2019

    Accepted: Aug. 16, 2019

    Published Online: Feb. 20, 2020

    The Author Email: Jianfeng Wang (291460700@qq.com), Hongwei Wang (3120759204@qq.com), Xueqin Yan (775456158@qq.com)

    DOI:10.3788/LOP57.041017

    Topics

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