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K. O. Sorokina, V. A. Fedorenko, P. V. Giverts Evaluation of the Similarity of Images of Breech Face Marks Using the Method of Correlation Cells |
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Abstract. The comparison of cartridge cases is required in the course of criminal investigations of cases associated with illegal use of firearms. Lately, have been developed a few methods of forensic investigation of the marks presented on the digital images of discharged cartridge cases. However, it is essential to improve the quality of the comparison of the marks presented on digital images. The aim of the research is development of a more effective method for evaluation of the similarity of marks presented on the primers surface of cartridge cases and produced by the breech face of the weapon. The paper presents a new method of correlation cells. According to the method the investigated images are divided into small cells, the areas with insufficient information are excluded from the comparison. This approach allows to improve the sensitivity of the comparison and to get the additional information about the correlation between the marks on the compared images. On the bases of the calculated likelihood ratio the criteria of matching and not-matching were defined. The presented method is intended to be used for searching similar breech face marks in the database of digital images of the cartridge cases. Keywords: Correlation coefficient, autocorrelation function, digital image processing, correlation cells method, breech face marks, cluster, likelihood ratio PP. 3-15. DOI 10.14357/20718632190301 References 1. Song J. 2013. Proposed “NIST Ballistics Identification System (NBIS)” Based on 3D Topographic Measurements on Correlation Cells. AFTE Journal. 45(2):184-194. 2. Song J. 2015. Proposed “Congruent Matching Cells (CMC)” Method for Ballistic Identification and Error Rate Estimation. AFTE Journal. 47(3):177-185. 3. Nichols R. 2018. Firearm and toolmark identification: the scientific reliability of the forensic science discipline. London: Academic Press. 170 p. 4. Fedorenko V. A., Gvozdkov S. N., Grabovec E. E. 2018. Vliyanie neodnorodnostej poverhnosti kapsyulej na variativnost' staticheskih sledov bojkov [Influence of Inhomogeneities of the Surface of Caps on Variability of Static Firing Pin Traces]. Izv. Saratov Univ. (N.S.), Ser. Economics. Management. Law. 18(2):202–207. 5. Song J., Vorburger T. V., Chua W., Yenb J., Soonsa J. A. 2018. Estimating error rates for firearm evidence identifications in forensic science. Forensic Science International. 284:15-32. 6. Kokin A. V., Yarmak K. V. 2015. Sudebnaya ballistika i sudebno-ballisticheskaya ekspertiza [Forensic ballistics and forensic ballistic examination. Textbook]. Moscow: Uniti-Dana. 351 p. 7. Oblasti primeneniya tekhnologii «POISK» i zadachi ballisticheskoj ekspertizy, reshaemye s pomoshch'yu dannoj tekhnologii [The range of the POISC Technology application and the ballistic expertise issues to be solved with this Technology]. Available at: http://www.sbc-spb.com (accessed July 1, 2019). 8. BRASSTRAX. The fastest, most accurate way to acquire cartridge case evidence. Available at: https://www.ultraforensictechnology. com/en/our-products/ballisticidentification/ brasstrax.htm (accessed July 1, 2019). 9. Fedorenko V. A., Kornilov M. V. 2015. Ocenka skhozhesti sledov bojkov ognestrel'nogo oruzhiya po ih cifrovym izobrazheniyam [Assessing similarities firing pin traces of firearms on their digital images]. Informacionnye tekhnologii i vychislitel'nye sistemy [Information technology and computing systems]. 3:92–100. 10. Gonzalez R. C., Woods R. E., Eddins S. L. 2009. Digital Image Processing Using MATLAB. London: Gatesmark Publishing. 827 p. 11. Zalewski E. N. 2015. Mathematics in Forensic Firearm Examination. Syracuse: Syracuse University SURFACE. 72 p. 12. Aitken C. G. G., Taroni F. Statistics and the Evaluation of Evidence for Forensic Scientists. 2004. New York: Wiley. 540 p.
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