An algorithm for multistage projective transformation adjustment for image superimposition
A.I. Efimov, A.I. Novikov

 

Ryazan State Radio Engineering University, Ryazan, Russia

Full text of article: Russian language.

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Abstract:
We propose a multistage algorithm for constructing a projective transformation based on arbitrary sets of keypoint pairs, with the sets containing more than four keypoint pairs. In the course of calculation of a homography matrix, unsuccessful keypoint pairs are detected and rejected. Experimental results on image superimposition using projective transformations for homogeneous and heterogeneous images are presented. Methods for assessing the quality of image superimposition, which include  the local and integral estimates of the superimposition quality, are proposed.

Keywords:
mixed-spectrum images, contour analysis methods, keypoints (special, corresponding), projective transformations, homography matrix, quality of image superimposition.

Citation:
Efimov AI, Novikov AI. An algorithm for multistage projective transformation adjustment for image superimposition. Computer Optics 2016; 40(2): 258-265. DOI: 10.18287/2412-6179-2016-40-2-258-265.

References:

  1. Baklickiy VK. Correlation-extreme methods of navigation and guidance [In Russian]. Tver: “Knizhniy Klub” Publisher; 2009.
  2. Furman YaA, Kreveckiy AV, Peredreev AK, Rozhencov AA, Hafazov RG, Egoshina IL, Leuhin AN. Introduction to the contour analysis. Applications to image and signal processing [In Russian]. Moscow: “Fizmatlit” Publisher; 2003.
  3. Gruzman IS, Kirichuk VS, Kosih VP, Peretryagin GI, Spector AA. Digital image processing in information systems [In Russian]. Novosibirsk: “Novosibirsk State Technical University” Publisher; 2002.
  4. Goshin EV, Kotov AP, Fursov VA. Two-stage formation of a spatial transformation for image matching. Computer Optics 2014; 38(4): 886-891.
  5. Alpatov BA, Babayan PV. Digital adjustment of multispectral images observation [In Russian]. Digital signal processing 2003; 1: 24-26.
  6. Novikov AI, Sablina VA, Nikiforov MB, Loginov AA. The contour analysis and image-superimposition problem in computer vision systems. Pattern Recognition and Image Analysis 2015; 25(1): 73-80. DOI: 10.1134/S1054661815020194
  7. Hast A, Nysjö J, Marchetti A. Optimal RANSAC – towards a repeatable algorithm for finding the optimal set. Journal of WSCG 2013; 21(1): 21-30.
  8. Novikov AI, Sablina VA, Efimov AI. Image Superimposition and the Problem of Selecting the Set of Corresponding Point Pairs. Proceedings of MECO 2015: 139-142.
  9. Herbert B, Andreas E, Tinne T, Van Gool L. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 2008; 110(3): 346-359.
  10. Lowe, DG. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 2004; 60(2): 91-110.
  11. Demidovich BP, Maron IA. Fundamentals of Computational Mathematics [In Russian]. Moscow: “Fizmatgiz” Publisher; 1963.
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