• Laser & Optoelectronics Progress
  • Vol. 55, Issue 6, 061002 (2018)
Lin Wang1、2、1; 2; and Qiang Liu1、2、1; 2;
Author Affiliations
  • 1 School of Microelectronics, Tianjin University, Tianjin 300072, China
  • 2 Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin 300072, China
  • show less
    DOI: 10.3788/LOP55.061002 Cite this Article Set citation alerts
    Lin Wang, Qiang Liu. A Multi-Object Image Segmentation Algorithm Based on Local Features[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061002 Copy Citation Text show less
    Flow chart of the proposed algorithm
    Fig. 1. Flow chart of the proposed algorithm
    (a) Background image; (b) scene image; (c) result with fixed threshold; (d) result with adaptive threshold
    Fig. 2. (a) Background image; (b) scene image; (c) result with fixed threshold; (d) result with adaptive threshold
    Eight experimental scenarios
    Fig. 3. Eight experimental scenarios
    Segmentation results obtained by different algorithms for the scene No. 5. (a) Algorithm in Ref. [5]; (b) FCN algorithm; (c) proposed algorithm with preprocessing; (d) proposed algorithm without preprocessing
    Fig. 4. Segmentation results obtained by different algorithms for the scene No. 5. (a) Algorithm in Ref. [5]; (b) FCN algorithm; (c) proposed algorithm with preprocessing; (d) proposed algorithm without preprocessing
    Segmentation results obtained by different algorithms for the scene No. 8. (a) Algorithm in Ref. [5]; (b) FCN algorithm; (c) proposed algorithm with preprocessing; (d) proposed algorithm without preprocessing
    Fig. 5. Segmentation results obtained by different algorithms for the scene No. 8. (a) Algorithm in Ref. [5]; (b) FCN algorithm; (c) proposed algorithm with preprocessing; (d) proposed algorithm without preprocessing
    ParameterAdaptivethresholdFixed threshold
    θS=150θS=200θS=250
    Number of feature points50405589
    Number of mismatching feature points12313
    Mismatching rate /%2.05.05.514.6
    Table 1. Comparison of matching points between adaptive threshold and fixed threshold
    SceneMismatching rate /%
    Original SIFTalgorithmProposed SIFTalgorithm
    118.91.5
    210.00
    39.31.7
    49.70
    514.32.0
    610.41.9
    722.51.2
    819.82.7
    Average13.21.4
    Table 2. Comparison of mismatching rate between the proposed SIFT algorithm and the original SIFT algorithm
    Scene12345678Average
    Proposedalgorithm (withpreprocessing)RO /%3.404.387.496.479.849.869.189.287.49
    RU /%000000000
    RE /%3.404.387.496.479.849.869.189.287.49
    Proposedalgorithm(withoutpreprocessing)RO /%3.404.317.496.178.959.837.878.097.01
    RU /%01.7000.851.290.262.651.200.99
    RE /%3.406.117.497.0810.3710.1210.809.408.10
    FCNalgorithmRO /%2.421.173.534.045.945.727.704.704.40
    RU /%1.124.362.952.083.342.541.823.812.74
    RE /%3.586.796.686.259.488.479.698.857.47
    Algorithmin Ref. [5]RO /%18.1925.0020.3923.2614.2631.8814.1111.7719.86
    RU /%023.1215.7913.0410.2026.673.2315.4413.44
    RE /%18.1962.5942.9641.7427.2579.8317.9232.1840.33
    Table 3. Comparison of segmentation errors among FCN algorithm, the algorithm in Ref. [5] and the proposed algorithms
    AlgorithmPreprocessingStereomatchingRegion refinement basedon depth informationSIFTmatchingMean shiftTotal
    Withpreprocessing0.380.060.060.490.021.01
    Withoutpreprocessing0.060.061.510.021.65
    Table 4. Runtime of each stage of the proposed algorithm (unit: s)
    Lin Wang, Qiang Liu. A Multi-Object Image Segmentation Algorithm Based on Local Features[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061002
    Download Citation