• Journal of Applied Optics
  • Vol. 44, Issue 1, 46 (2023)
Yi GAO1, Ying YU2,*, Xu YANG3, and Shuangxi XIANG3
Author Affiliations
  • 1Key Laboratory of Trace Inspection and Identification Technology (Ministry of Public Security), Criminal Investigation Police University of China, Shenyang 110035, China
  • 2Key Laboratory of Optoelectronic Information Control and Security Technology, Tianjin 300308, China
  • 3School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
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    DOI: 10.5768/JAO202344.0101007 Cite this Article
    Yi GAO, Ying YU, Xu YANG, Shuangxi XIANG. Intelligent trace color separation method based on multi-band light source[J]. Journal of Applied Optics, 2023, 44(1): 46 Copy Citation Text show less
    Schematic block diagram of lifting motor for light source movement control
    Fig. 1. Schematic block diagram of lifting motor for light source movement control
    Schematic diagram of experimental platform
    Fig. 2. Schematic diagram of experimental platform
    Flow chart of overall system design
    Fig. 3. Flow chart of overall system design
    Diagram of six roles
    Fig. 4. Diagram of six roles
    Flow chart of FS-SIFT algorithm
    Fig. 5. Flow chart of FS-SIFT algorithm
    Process of secondary generation Curvelet fusion algorithm
    Fig. 6. Process of secondary generation Curvelet fusion algorithm
    Partial images of moving trace photography in vertical direction of light source
    Fig. 7. Partial images of moving trace photography in vertical direction of light source
    Partial images of moving trace photography in horizontal direction of light source
    Fig. 8. Partial images of moving trace photography in horizontal direction of light source
    Photography process of sweat latent fingerprint of gray-white Matt ceramic tile
    Fig. 9. Photography process of sweat latent fingerprint of gray-white Matt ceramic tile
    Image index analysis in horizontal direction
    Fig. 10. Image index analysis in horizontal direction
    Color separation photography of potential fingerprints on surface of orange-red ceramic tiles
    Fig. 11. Color separation photography of potential fingerprints on surface of orange-red ceramic tiles
    Color separation photography of potential fingerprints on surface of light blue ceramic tiles
    Fig. 12. Color separation photography of potential fingerprints on surface of light blue ceramic tiles
    Color separation photography of oil latent fingerprints on surface of light yellow cards
    Fig. 13. Color separation photography of oil latent fingerprints on surface of light yellow cards
    Feature point extraction of traditional SIFT algorithm
    Fig. 14. Feature point extraction of traditional SIFT algorithm
    Feature point extraction of improved FS-SIFT algorithm
    Fig. 15. Feature point extraction of improved FS-SIFT algorithm
    Image registration with traditional SIFT algorithm and image registration with improved FS-SIFT algorithm
    Fig. 16. Image registration with traditional SIFT algorithm and image registration with improved FS-SIFT algorithm
    Comparison of fusion trace images of secondary generation Curvelet algorithm
    Fig. 17. Comparison of fusion trace images of secondary generation Curvelet algorithm
    标准差信息熵平均梯度
    暗视场11.322.051 40.018 7
    最佳位置14.472.372 80.020 6
    Table 1. Comparison of indicators of dark field and optimal position trace image
    标准差信息熵平均梯度
    定向反射17.143.100 70.009 1
    最佳位置19.883.631 40.010 3
    Table 2. Comparison of indicators of directional reflection and optimal position trace image
    标准差信息熵平均梯度
    掠入射17.143.100 70.009 1
    最佳位置19.883.631 40.010 3
    Table 3. Comparison of indicators of grazing incidence and optimal position trace image
    标准差信息熵平均梯度
    最佳位置图像21.0810.402 70.071 3
    最佳位置90°图像20.9310.396 40.071 0
    Table 4. Objective evaluation indicators of the best position image and the best position 90 ° image
    特征点正确匹配点匹配准确率/%时间/s
    SIFT最佳位置64332751.8510.3
    最佳位置90°64333451.9410.1
    FS-SIFT最佳位置57752591.403.5
    最佳位置90°57751990.103.6
    Table 5. Characteristic accuracy and time consumption of traditional SIFT algorithm and FS-SIFT algorithm
    标准差信息熵平均梯度
    SIFT算法配准18.748.325 10.064 9
    FS-SIFT算法配准23.2714.136 30.094 4
    Table 6. Objective evaluation indicators of fused image of secondary generation Curvelet algorithm
    Yi GAO, Ying YU, Xu YANG, Shuangxi XIANG. Intelligent trace color separation method based on multi-band light source[J]. Journal of Applied Optics, 2023, 44(1): 46
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