Alternate-step two-gradient algorithm for aligning images of objects by reference points

Samoylin, E.A., Kushchev, S.S., Karpov, S.A.

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Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Самойлин Е.А., Кущев С.С., Карпов С.А. Черезшаговый двухградиентный алгоритм совмещения изображений объектов по реперным точкам // Оптический журнал. 2019. Т. 86. № 7. С. 11–18. http://doi.org/10.17586/1023-5086-2019-86-07-11-18

Samoylin E.A., Kushchev S.S., Karpov S.A. Alternate-step two-gradient algorithm for aligning images of objects by reference points [in Russian] // Opticheskii Zhurnal. 2019. V. 86. № 7. P. 11–18. http://doi.org/10.17586/1023-5086-2019-86-07-11-18

For citation (Journal of Optical Technology):

E. A. Samoĭlin, S. S. Kushchev, and S. A. Karpov, "Alternate-step two-gradient algorithm for aligning images of objects by reference points," Journal of Optical Technology. 86(7), 401-407 (2019). https://doi.org/10.1364/JOT.86.000401

Abstract:

An alternate-step algorithm is proposed for aligning an original object with a reference object. In this algorithm, gradient adjustment of the reference points of the original object in the direction of the coordinates of the reference points of the reference object is performed at odd steps, while at even steps, gradient adjustment is done in the directions that allow preservation of the original shape of the displaced object. An example of such an alternate-step two-gradient alignment of two images of objects with different values of speed and stability of convergence is presented.

Keywords:

images aligning, reference points, coordinates adjustment, gradient search, alternate-step two-gradient algorithm

OCIS codes: 100.2000

References:

1. K. K. Vasilyev and V. R. Krasheninnikov, Statistical Analysis of Image Sequences (Radiotekhnika, Moscow, 2017).
2. R. Gonzalez and R. Woods, Digital Image Processing (Tekhnosfera, Moscow, 2012).
3. B. N. Epifantsev and V. S. Lyakhovskiı˘, “Detection of simply shaped objects on stationary images of an underlying surface by an interpreter/operator and by a computer vision system,” J. Opt. Technol. 83(1), 49–54 (2016) [Opt. Zh. 83(1), 65–72 (2016)].
4. N. N. Lapina, V. R. Lutsiv, I. A. Malyshev, and A. S. Potapov, “Features of image comparison in problems of determining the location of a mobile robot,” J. Opt. Technol. 77(11), 677–683 (2010) [Opt. Zh. 77(11), 25–34 (2010)].
5. P. A. Chochia, “Rapid correlation alignment of quasi-regular images,” Inf. Process. 9(3), 117–120 (2009).
6. L. N. Kostyashkin, A. A. Loginov, and M. B. Nikiforov, “Problem aspects of the system of combined vision of aircraft,” Izv. YuFU 5, 61–65 (2013).
7. S. I. Elesina, L. N. Kostyashkin, A. A. Loginov, and M. B. Nikiforov, Combination of Images in Correlation-Extreme Navigation Systems (Radiotekhnika, Moscow, 2015).
8. A. S. Potapov, I. A. Malyshev, and V. R. Lutsiv, “Bringing aerospace images into coincidence with subpixel accuracy by the local-correlation method,” J. Opt. Technol. 71(5), 293–297 (2004) [Opt. Zh. 71(5), 31–36 (2004)].
9. A. I. Novikov, V. A. Sablina, and E. O. Goryachev, “The use of contour analysis to combine images,” Izv. TSU Tekh. Nauki 9(1), 260–270 (2013).
10. A. I. Efimov and A. I. Novikov, “Algorithm of step-by-step refinement of the projective transformation for combining images,” Komp. Opt. 40(2), 258–265 (2016).
11. E. V. Goshin, A. P. Kotov, and V. A. Fursov, “Two-stage spatial conversion for image alignment,” Komp. Opt. 38(4), 886–891 (2014).
12. B. Widrow and S. Stirnz, Adaptive Signal Processing, translated from English (Radio i Svyaz’, Moscow, 1989).
13. E. A. Samoı˘lin and M. A. Pantyukhin, “Method of noise-resistant gradient selection of the contours of objects in digital images,” Russian patent 2589301 (2016).
14. D. Loeckx, F. Maes, D. Vandermeulen, and P. Suetens, “Nonrigid image registration using free-form deformations with a local rigidity constraint,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI) (Springer, Berlin, 2004), pp. 639–646.
15. Y. Ke and R. Sukthankar, “PCA-SIFT: a more distinctive representation for local image descriptors,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2004), pp. 506–513.
16. A. Hast, J. Nysj, and A. Marchetti, “Optimal RANSAC—towards a repeatable algorithm for finding the optimal set,” J. WSCG 21(1), 21–30 (2013).