Adaptive matching algorithm for laser detection signals of linear cutting tool marks

Adaptive matching algorithm for laser detection signals of linear cutting tool marks

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Since it is very difficult to compare linear cutting tool marks quickly and quantitatively using existing image-processing and three-dimensional scanning methods, an adaptive matching algorithm for laser detection signals of linear cutting marks is proposed. Using locally weighted scatterplot smoothing regression, the proposed algorithm first performs noise reduction on the surface signals of linear cutting tool marks that are detected by a laser displacement sensor. Trends of the thick and consistent features of the signal data are then identified and the feature vectors are quantised via cosine vector curve fitting to individually calculate the spatial distances of the samples. Finally, the most similar samples are matched via batch similarity comparison using a dynamic programming (DP) algorithm. The accuracy and validity of the proposed algorithm are verified by similarity comparison tests of actual cutting marks from a variety of samples.


    1. 1)
      • 1. Yang, M., Peng, Y.: ‘Toolmarks system construction based on experts knowledge’, J. People's Public Secur. Univ. China (Sci. Technol.), 2014, 2, (2), pp. 15.
    2. 2)
      • 2. Bunch, S., Wevers, G.: ‘Application of likelihood ratios for firearm and toolmark analysis’, Sci. Justice, 2013, 53, pp. 223229.
    3. 3)
      • 3. Yang, M., Li, D.-Y., Wang, W.-D.: ‘The study of tool mark identification based on local wavelet energy’, J. People's Public Secur. Univ. China (Sci. Technol.), 2008, 56, (2), pp. 7375.
    4. 4)
      • 4. Kassamakov, I., Barbeau, C., Lehto, S., et al: ‘CSI Helsinki: Comparing three-dimensional imaging of diagonal cutter toolmarks using confocal microscopy and SWLI’. Three-Dimensional Imaging, Visualization, and Display 2010 and Display Technologies and Applications for Defense, Security, and Avionics IV, Orlando, FL, United States, 6–8 April 2010, p. 7690: 76900Y.
    5. 5)
      • 5. Lock, A.-B., Morris, M.-D.: ‘Significance of angle in the statistical comparison of forensic tool marks’, Technometrics, 2013, 55, (4), pp. 548561.
    6. 6)
      • 6. Gambino, C., McLaughlin, P., Kuo, L., et al: ‘Forensic surface metrology: tool mark evidence’, Scanning, 2011, 33, (5), pp. 272278.
    7. 7)
      • 7. Robert, F., Clau, V.: ‘Forensic ballistic analysis using a 3D sensor device’. 14th ACM Multimedia and Security Workshop, MM and Sec 2012, Coventry, United Kingdom, 6–7 September 2012, pp. 6775.
    8. 8)
      • 8. Heizmann, M.: ‘Techniques for the segmentation of striation patterns’, IEEE Trans. Image Process., 2006, 15, (3), pp. 624631.
    9. 9)
      • 9. Jia, Z.-H., Xiang, H.: ‘Computer aided programming for comparative examination of line traces’, Sci. Technol. Eng., 2007, 7, (24), pp. 63536355, 6361.
    10. 10)
      • 10. Quan, G.-T., Hao, R.-X., Yu, X.: ‘Method of indoor-calibration about laser ranging accuracy’, Foreign Electron. Meas. Technol., 2013, 32, (9), pp. 4245.
    11. 11)
      • 11. Jiang, L.-T., Xu, G.-Z., Zhou, L.-L.: ‘Method of software aging trend based on robust locally weighted regression algorithm’, J. Shanghai Jiaotong Univ., 2007, 40, (11), pp. 19511954.
    12. 12)
      • 12. Willam, S.C.: ‘Robust locally weighted regression and smoothing scatterplots’, J. Am. Stat. Assoc., 1979, 74, pp. 829836.
    13. 13)
      • 13. McArthur, J.M., Howarth, R.J.: ‘Strontium isotope stratigraphy: LOWESS version 3: best fit to the marine Sr2lsotope curve for 0509 Ma and accompanying Look2up Table for deriving numerical age’, J. Geol., 200l, 109, pp. 155170.
    14. 14)
      • 14. Gu, T.-Q., Zhou, Z.-L., Ji, S.-J., et al: ‘Cure fitting method for closed discrete points’, J. Jilin Univ. (Eng. Technol. edition), 2015, 45, (2), pp. 437441.
    15. 15)
      • 15. Pan, N., Wu, X., Liu, Y., et al: ‘Gear box combined failure acoustical diagnosis based on frequency domain blind deconvolution’, J. Vib. Shock, 2013, 32, (7), pp. 154158.
    16. 16)
      • 16. Li, Y.-C., W, X., Liu, X.-Q., et al: ‘Blind separation for bearing faults based on morphological filters and ICA’, Electron. Meas. Technol., 2010, 33, (9), pp. 101103, 113.
    17. 17)
      • 17. Zhou, Z.-C., Zhang, Y.-H.: ‘Channel model changes detecting method based on DP-algorithm’, ACTA Electron. Sinica, 2011, 39, (1), pp. 157161.
    18. 18)
      • 18. Raj, R.G., Chen, V.C., Lipps, R.: ‘Analysis of radar human gait signatures’, IET Signal Process., 2010, 4, (3), pp. 234244.
    19. 19)
      • 19. Li, Y.-Z., Zhang, J.: ‘New intrusion detection algorithm based on cluster and cloud model’, J. Electron. Meas. Instrum., 2014, 28, (12), pp. 13761381.

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