Izvestiya of Saratov University.

Economics. Management. Law

ISSN 1994-2540 (Print)
ISSN 2542-1956 (Online)

For citation:

Fedorenko V. A., Kornilov M. V. Allocation of Individual Attributes on Digital Images of the Firing Pin Traces. Journal Izvestiya of Saratov University. Economics. Management. Law, 2014, vol. 14, iss. 1, pp. 181-186. DOI: 10.18500/1994-2540-2014-14-1-2-181-186

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Allocation of Individual Attributes on Digital Images of the Firing Pin Traces

Fedorenko V. A., Saratov State University
Kornilov M. V., Saratov State University

Introduction. Automated ballistic identification systems allow automation of inspections by array firing pin traces containing thousands of similar objects. However, sometimes the system allow «mistakes», ie can not find ‘doubles’ trail in database. In addition, quite often «doubles» trail of test array is placed on the priority list is far from its beginning. This is due, primarily, large morphological variety and high variability of individual attributes of weapons displayed in the firing pin tracks, as well as uneven lighting traces due to their complex forms. Theoretical analysis. Studies have shown that non-uniformity of brightness of digital images traces of the firing pin can be smoothed by applying the homomorphic image processing method. Analysis of the morphology of individual signs weapons displayed in the tracks of the strikers 30 models of weapons, allowed to identify six main morphological types of individual attributes. Experimental investigation. Efficient algorithms for feature extraction of the form of large irregularly shaped spots by applying the Wiener filter and method Niblek developed. For feature extraction in the form of circles, we propose a method based on the use of the Canny filter. Conclutions. Studies have shown: homomorphic method of digital image processing can be recommended for pre-processing raw images; classification of morphological types of individual signs developed; algorithms binarization images features in the form of large irregularly shaped areas and in the form of circles developed.

  1. Fisenko T. Yu., Fisenko V. T. Issledovanie i razrabotka metodov uluchsheniya podvodnych isobrazheniy (Research and development of methods of improvement of underwater images). Filial OAO «Korporatsiya “Kometa” – nauchno-proektniy centr optoelectronnich kompleksov nabludeniya» [Branch of OJSC «Corporation “Comet” – research and design center of optoelectronic surveillance complexes»], St.-Petersburg, 2011, pp. 294–298. Available at: http://www.oop-ros.org/maket2012/part7/7.18.pdf (accesses 10 March 2014).
  2. Gonzales Rafael C., Woods Richard E. Digital Image Processing. 2nd ed., Prentice Hall, Upper Saddle River, N.J., 2002 (Russ. ed.: Gonsales R., Vuds R. Tsifrovaia obrabotka izobrazhenii. Moscow, Tekhnosfera Publ., 2005. 1072 p.).