Efficiency of object identification for binary images
Magdeev R., Tashlinskii Al.

Ulyanovsk State Technical University, Russia, Ulyanovsk,
Telekom.ru LLC, Russia, Ulyanovsk

In this paper, a comparative analysis of the correlation-extreme method, the method of contour analysis and the method of stochastic gradient identification in the objects identification for a binary image is carried out. The results are obtained for a situation where possible deformations of an identified object with respect to a pattern can be reduced to a similarity model, that is, the pattern and the object may differ in scale, orientation angle, shift along the base axes, and additive noise. The identification of an object is understood as the recognition of its image with an estimate of the strain parameters relative to the template.

Ключевые слова:
digital image, object recognition, pattern recognition, correlation-extreme algorithm, stochastic gradient identification, incorrect identification probability.

Magdeev RG, Tashlinskii AG. Efficiency of object identification for binary images. Computer Optics 2019; 43(2): 277-281. DOI: 10.18287/2412-6179-2019-43-2-277-281.


  1. Poltavskii, A.V. Basics of pattern recognition using computer [In Russian] / A.V. Poltavskii, A.V. Grinshkun // Dvoinie Tehnologii. – 2017. – Vol. 2. – P. 55-66.
  2. Knyaz, V.A. Intelligent information processing technologies for navigation and control problems of unmanned aerial vehicles [In Russian] / V.A. Knyaz, B.V. Vishnyakov, Y.V. Vizilter, V.S. Gorbancevich, O.V. Vigolov // Trudi SPIIRAN. – 2016. – Vol. 45. – P. 26-44. – DOI: 10.15622/sp.45.2.
  3. Kuznetsov, A.V. A copy-move detection algorithm based on binary gradient contours / A.V. Kuznetsov, V.V. Myasnikov // Computer Optics. – 2016. – Vol. 40, Issue 2. – P. 284-293. – DOI: 10.18287/2412-6179-2016-40-2-284-293.
  4. Magdeev, R.G. A comparative analysis of the efficiency of the stochastic gradient approach to the identification of objects in binary images / R.G. Magdeev, A.G. Tashlinskii // Pattern Recognition and Image Analysis. – 2014. – Vol. 24, Issue 4. – P. 535-541. – DOI: 10.1134/S1054661814040130.
  5. Prett, W. Digital image processing: in 2 volumes / W. Prett. – New York: John Wiley and Sons, 1978.
  6. Gruzman, I.S. Digital image processing in information systems [In Russian] / I.S. Gruzman, V.S. Kirichuk, V.P. Kosih, G.I. Peretyagin, A.A. Spektor. – Novosibirsk: NGTU Publisher, 2002.
  7. Furman, Ya.A. Introduction to contour analysis and its applications to image and signal processing [In Russian] / Ya.A. Furman, A.V. Krevetsky, A.K. Peredeyev, A.A. Rozhentsov, R.G. Khafizov, I.L. Egoshina, A.L. Leukhin. – Moscow: “Fizmatlit” Publisher; 2003.
  8. Tsypkin, Ya.Z. Information theory of identification [In Russian] / Ya.Z. Tsypkin. – Moscow: “Fizmatlit” Publisher; 1995.
  9. Tashlinskii, A.G. Computational expenditure reduction in pseudo-gradient image parameter estimation / A.G. Tashlinskii // International Conference on Computational Science 2003. – 2003. – Vol. 2658. – P. 456-462.
  10. Gonzalez, R. Digital image processing / R. Gonzalez, R. Woods. – Upper Saddle River: New Jersey: Prentice Hall; 2012.
  11. Canny, J.A. computational approach to edge detection / J.A. Canny // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1986. – Vol. PAMI-8(6). – P. 679-698.
  12. Duda, R.O. Pattern classification / R.O. Duda, P.E. Hart, D.G. Stork. – New York: Wiley-Interscience; 2001.
  13. Tashlinskii, A.G. Pseudogradient estimation of digital images interframe geometrical deformations / A.G. Tashlinskii. – In: Vision Systems: Segmentation and Pattern Recognition / ed. by G. Obinata, A. Dutta. – Vienna: InTech, 2007. – P. 465-494. – DOI: 10.5772/4975.
  14. Tashlinskii, A.G. The specifics of pseudogradient estimation of geometric deformations in image sequences / A.G. Tashlinskii // Pattern Recognition and Image Analysis. – 2008. – Vol. 18, Issue 4. – P. 700-705. – DOI: 10.1134/S1054661808040275.
  15. Tashlinskii, A.G. Estimation of the parameters of spatial deformations of image sequences [In Russian] / A.G. Tashlinskii. – Ulyanovsk: UlSTU Publisher; 2000.
  16. Fadeeva, G.L. Optimization of the pseudo-gradient of the objective function in the estimation of inter-frame geometric deformations of images [In Russian] : The thesis for the Candidate’s degree in Technical Sciences: 05.13.18. – Ulyanovsk; 2007.
  17. Sebryakov, G.G. Optimization of parameters of partitioning the analyzed fragment of the image of the scene according to the quality criteria and computational efficiency of recognition of the observed objects [In Russian] / Sebryakov GG, Soshnikov VN, Kikin IS, Ishutin AA. – In: Technical vision in control systems: materials of scientific and technical conference. – Moscow: SAKVOEE Space Research Institute of the Russian Academy of Sciences. – 2014. – P. 149-151.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: ko@smr.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20