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

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.

Inspec keywords: dynamic programming; signal detection; cutting tools; image matching; optical sensors; production engineering computing

Other keywords: image processing; adaptive matching algorithm; linear cutting tool marks; cosine vector curve fitting; three-dimensional scanning methods; laser detection signals; laser displacement sensor; dynamic programming algorithm

Subjects: Production equipment; Optimisation techniques; Image recognition; Optimisation techniques; Production engineering computing; Optimisation; Computer vision and image processing techniques; Industrial applications of IT

References

    1. 1)
      • 6. Gambino, C., McLaughlin, P., Kuo, L., et al: ‘Forensic surface metrology: tool mark evidence’, Scanning, 2011, 33, (5), pp. 272278.
    2. 2)
      • 17. Zhou, Z.-C., Zhang, Y.-H.: ‘Channel model changes detecting method based on DP-algorithm’, ACTA Electron. Sinica, 2011, 39, (1), pp. 157161.
    3. 3)
      • 12. Willam, S.C.: ‘Robust locally weighted regression and smoothing scatterplots’, J. Am. Stat. Assoc., 1979, 74, pp. 829836.
    4. 4)
      • 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.
    5. 5)
      • 8. Heizmann, M.: ‘Techniques for the segmentation of striation patterns’, IEEE Trans. Image Process., 2006, 15, (3), pp. 624631.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 18. Raj, R.G., Chen, V.C., Lipps, R.: ‘Analysis of radar human gait signatures’, IET Signal Process., 2010, 4, (3), pp. 234244.
    9. 9)
      • 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.
    10. 10)
      • 9. Jia, Z.-H., Xiang, H.: ‘Computer aided programming for comparative examination of line traces’, Sci. Technol. Eng., 2007, 7, (24), pp. 63536355, 6361.
    11. 11)
      • 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.
    12. 12)
      • 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.
    13. 13)
      • 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.
    14. 14)
      • 5. Lock, A.-B., Morris, M.-D.: ‘Significance of angle in the statistical comparison of forensic tool marks’, Technometrics, 2013, 55, (4), pp. 548561.
    15. 15)
      • 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.
    16. 16)
      • 2. Bunch, S., Wevers, G.: ‘Application of likelihood ratios for firearm and toolmark analysis’, Sci. Justice, 2013, 53, pp. 223229.
    17. 17)
      • 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.
    18. 18)
      • 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.
    19. 19)
      • 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.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2015.0372
Loading

Related content

content/journals/10.1049/iet-spr.2015.0372
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading