(47-1) 12 * << * >> * Russian * English * Content * All Issues

Iterative algorithm for accurate superposition of contours with non-uniform sampling step
R.R. Diyazitdinov 1

1 PSUTY – Povolzhskiy State University of Telecommunications and Informatics,
443010, Samara, Russia, Tolstoy str., 23

 PDF, 2864 kB

DOI: 10.18287/2412-6179-CO-1123

Pages: 102-111.

Full text of article: Russian language.

Abstract:
In this article, we describe an iterative algorithm for accurate superposition of contours with non-uniform sampling step. The processing contours are characterized by the same shape, but the sampling step is non-uniform, with no matching between points of the superposed contours. This makes impossible the use of methods for estimating superposition parameters by matching points. The algorithm proposed herein allows estimating the offsets and rotation angle separately. The idea of the algorithm is to perform the iterative correction of parameters. An estimate of the offsets is used to estimate the rotation angle and, vice versa, an estimate of the rotation angle is used to estimate the offsets. The proposed algorithm is characterized by a higher speed of processing than a brute force algorithm and a lower estimation error than algorithms that analyze contour macroparameters.

Keywords:
superposition, iterative, space-time, contour, accuracy.

Citation:
Diyazitdinov RR. Iterative algorithm for accurate superposition of contours with non-uniform sampling step. Computer Optics 2023; 47(1): 102-111. DOI: 10.18287/2412-6179-CO-1123.

References:
  1. Soifer VA, ed. Methods for computer image processing [In Russian]. Moskow: "Fizmatlit" Publisher; 2003.
  2. Sungatullina DI, Krilov AV. Fast algorithm for image contour superposition linking isotropic affine transformation [In Russian]. GRAFIKON 2014; 92-95.
  3. Efimov AI. Developing and researching image superposition algorithm for video sensors with a virtual terrain model [In Russian]. Ryazan: "Ryazan State Radio Engineering University" Publisher; 2016.
  4. Efimov AI, Novikov AI. An algorithm for multistage projective transformation adjustment for image superimposition [In Russian]. Computer Optics 2016; 40(2): 258-265. DOI: 10.18287/2412-6179-2016-40-2-258-265.
  5. Vasin NN, Diyazitdinov RR. Processing of triangulation scanner data for measurements of rail profiles [In Russian]. Computer Optics 2018; 42(6): 1054-1061. DOI: 10.18287/2412-6179-2018-42-6-1054-1061.
  6. Diyazitdinov RR. Video signal recovery in measuring systems with optical triangulation sensors [In Russian]. Infocommunication Technologies 2019; 17(3): 324-331. DOI: 10.18469/ikt.2019.17.3.09.
  7. DIN EN 13674-1-2011. Railway applications – Track – Rail – Part 1: Vignole railway rails 46 kg/m and above; German version EN 13674-1:2011.
  8. Diyazitdinov R. Iterative algorithm of optical triangulation sensors signals superposition for measuring solid deformation. CEUR Workshop Proceedings 2020; 2665: 93-99.
  9. Wang W, Jiang Y, Xiong B, Zhao L. Contour matching using the affine-invariant support point set. IET Computer Vision 2014; 8: 35-44. DOI: 10.1049/iet-cvi.2013.0031.
  10. Efimov AI, Novikov AI. Software and algorithmic system for image superposition in aircraft vision systems [In Russian]. The III Int Conf on Information Technology and Nanotechnology (ITNT-2017) 2017: 400-409.
  11. Furman YaA, Kreversky AV, Peredreyev AK, Rozentsov AA, Hafizov RG, Yegoshina IL, Leukhin AN. Contour analysis and its image and signal processing application [In Russian]. Moscow: "Fizmatlit" Publisher; 2003. ISBN: 5-9221-0374-1.
  12. Makarov MA. Contour analisys in the problems of description and classification of objects [In Russian]. Modern Problems of Science and Education. Surgery 2014; 3: 38-38.
  13. Ellis T, Abbood A, Brillault B. Ellipse detection and matching with uncertainty. Image Vis Comput 1992; 10(5): 271-276. DOI: 10.1016/0262-8856(92)90041-Z.
  14. Fitzgibbon A, Fisher R. A bayer's guide to conic fitting. Proc 6th British conf on Machine vision 1995; 2: 513-522.
  15. Gander W, Golub GH, Strebel R. Least-square fitting of circles and ellipses. BIT Numer Math 1994; 34(4): 558-578. DOI: 10.1007/BF01934268.
  16. Bookstein FL. Fitting conic sections to scattered data. Comput Graph Image Process 1979; 9(1): 56-71. DOI: 10.1016/0146-664X(79)90082-0.
  17. Linnik YuV. Least Squares method and base of mathematical and statistical theory of data processing [In Russian]. Moscow: "Fizmatgiz" Publisher; 1958.
  18. Baklitckiy VK. The method of signal filtering in correlation-extreme navigation systems [In Russian]. Tver: "Knigniy klub" Publisher; 2009.
  19. Myasnikov EV. Determination of parameters of geometric transformation to combine portrait images. Computer Optics 2007; 31(3): 77-82.
  20. Reddy B, Chatterji B. An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans Image Process 1996; 5(8): 1266-1271. DOI: 10.1109/83.506761.
  21. Alba A, Aguilar-Ponce R, Vigueras-Gomez J, Arce-Santana E. Phase correlation based image alignment with subpixel accuracy. In Book: Batyrshin I, Mendoza MG, eds. Advances in artificial intelligence. 11th Mexican Int Conf on Artificial Intelligence (MICAI 2012), Part 1 2012: 171-182. DOI: 10.1007/978-3-642-37807-2_15.
  22. Evangelidis G, Psarakis E. Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans Pattern Anal Mach Intell 2008; 30(10): 1858-1865. DOI: 10.1109/TPAMI.2008.113.
  23. Kuzmin SV. Scale-invariant delay estimation between two one-dimensional digital signals [In Russian]. Infocommunication Technologies 2011; 9(2): 7-10.
  24. Catalog optoNCDT laser sensors (Laser displacement sensors – triangulation). Source: <https://www.micro-epsilon.ru/download/products/cat--optoNCDT--en.pdf>.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20