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2D affine transform parameters by Gaussian elimination with pivoting

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In many geomatics, computer vision, and computer-aided applications, coordinate transformations are needed to transform from one coordinate system to another, especially in geodesy and photogrammetry. In photogrammetry one of the important coordinates transformation methods used to transform photo coordinates is the 2D affine transformation which takes into consideration the change in the differences in scale factor in the x and y directions. In this paper, a new method for computing the 2D affine transform parameters will be introduced, the problem of the 2D affine transform method has been solved by Gaussian elimination with pivoting.We have derived equations by which to find transformation parameters. Geometric transformation is a technique used to define the properties of common features between different images using the same coordinates basis, This method can be effectively used in image processing and computer vision to facilitate the computation process throughout eliminating the need for solving the inverse of the matrix.
Rocznik
Strony
113--127
Opis fizyczny
Bibliogr. 27 poz., il., tab.
Twórcy
  • University of Technology, Civil Engineering Department, Baghdad, Iraq
autor
  • University of Technology, Civil Engineering Department, Baghdad, Iraq
  • University of Technology, Civil Engineering Department, Baghdad, Iraq
Bibliografia
  • [1] O. Akyilmaz, “Total least squares solution of coordinate transformation”, Survey Review, vol. 39, no. 303, pp. 68-80, 2007, doi: 10.1179/003962607X165005.
  • [2] P.R.Wolf, B.A. Dewitt, and B.E.Wilkinson, Elements of Photogrammetry with Applications in GIS. McGraw-Hill Education, 2014.
  • [3] H.-G. Maas, “Concepts of real-time photogrammetry”, Human Movement Science, vol. 16, no. 2-3, pp. 189-199, 1997, doi: 10.1016/S0167-9457(96)00049-8.
  • [4] I. Piech, T. Adam, and P. Dudas, “3D modelling with the use of photogrammetric methods”, Archives of Civil Engineering, vol. 68, no. 3, pp. 481-500, 2022, doi: 10.24425/ace.2022.141898.
  • [5] W.A. Gaman and W.A. Giovinazzo, “Coordinate systems and transformations”, in PHIGS by Example. Springer, 1991, pp. 27-69.
  • [6] J.S. Greenfeld, “Least squares weighted coordinate transformation formulas and their applications”, Journal of Surveying Engineering, vol. 123, no. 4, pp. 147-161, 1997, doi: 10.1061/(ASCE)0733-9453(1997)123:4(147).
  • [7] O. Andrei, “3D affine coordinate transformations”, Master’s of Science, School of Architecture and the Built Environment Royal Institute of Technology (KTH), 100 44 Stockholm, Sweden, 2006.
  • [8] J. Cao, K. Zhang, H. Yong, X. Lai, B. Chen, and Z. Lin, “Extreme learning machine with affine transformation inputs in an activation function”, IEEE Transactions on Neural Networks Learning Systems, vol. 30, no. 7, pp. 2093-2107, 2018, doi: 10.1109/tnnls.2018.2877468.
  • [9] K. Mikolajczyk and C. Schmid, “An affine invariant interest point detector”, in European Conference on Computer Vision. Springer, 2002, pp. 128-142.
  • [10] T. Schenk, Introduction to photogrammetry. The Ohio State University, 2005. [Online]. Available: https://www.mat.uc.pt/~gil/downloads/IntroPhoto.pdf. [Accessed: 15 Feb. 2024].
  • [11] D. Turner, A. Lucieer, and C. Watson, “An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds”, Remote Sensing, vol. 4, no. 5, pp. 1392-1410, 2012, doi: 10.3390/rs4051392.
  • [12] G. Wang, B. Zheng, X. Li, Z. Houkes, and P.P. Regtien, “Modelling and calibration of the laser beam-scanning triangulation measurement system”, in Robotics and Autonomous Systems, vol. 40, no. 4, pp. 267-277, 2002, doi: 10.1016/S0921-8890(02)00247-6.
  • [13] M. Kowalska, J. Zaczek-Peplinska, and Ł. Piasta, “Determining the trend of geometrical changes of a hydrotechnical object based on data in the form of LiDAR point clouds”, Archives of Civil Engineering, vol. 70, no. 1, pp. 305-323, 2024, doi: 10.24425/ace.2024.148913.
  • [14] R.E. Deakin, “3-D coordinate transformations”, Surveying Land Information Systems, vol. 58, no. 4, pp. 223-234, 1998.
  • [15] B. Konakoglu, L. Cakýr, and E. Gökalp, “2D coordinate transformation using artificial neural networks”, The International Archives of Photogrammetry, Remote Sensing Spatial Information Sciences, vol. 42, pp. 183-186, 2016, doi: 10.5194/isprs-archives-XLII-2-W1-183-2016.
  • [16] K. Novak, “Rectification of digital imagery”, Photogrammetric Engineering and Remote Sensing, vol. 58, no. 3, pp. 339-344, 1992. Available: https://www.asprs.org/wp-content/uploads/pers/1992journal/mar/1992_mar_339-344.pdf. [Accessed: 15 Feb. 2024].
  • [17] K. Noga, A. Sikora, M. Siejka, E. Natividade-Jezus, C. Moreira, and I. Skrzypczak, “Identification of land system transformations in the Rzeszów city square”, Archives of Civil Engineering, vol. 69, no. 2, pp. 607-621, 2023, doi: 10.24425/ace.2023.145287.
  • [18] I.H. Mohammed, T.N. Ataiwe, and H. Al Sharaa, “Accuracy assessment of a variety of GPS data processing, online services and software”, Geomatics and Environmental Engineering, vol. 15, no. 4, pp. 5-19, 2021, doi: 10.7494/geom.2021.15.4.5.
  • [19] H.M.J. Al Sharaa, T.N. Ataiwe, and I.H. Mohammed, “Evaluation of solid waste management using geomatics techniques (Al Muthanna governorate – case study)”, Journal of Green Engineering, vol. 11, no. 2, pp. 1778-1796, 2021.
  • [20] B. Schaffrin and Y.A. Felus, “On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms”, in Journal of Geodesy, vol. 82, no. 6, pp. 373-383, 2008, doi: 10.1007/s00190-007-0186-5.
  • [21] P.K. Tadavani and A. Ghodsi, “Learning an affine transformation for non-linear dimensionality reduction”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2010, pp. 19-34.
  • [22] J.R. Jensen, Introductory digital image processing: a remote sensing perspective, 2nd ed. Prentice-Hall Inc., 1996.
  • [23] X. Li, Y. Xu, Q. Lv, and Y. Dou, “Affine-transformation parameters regression for face alignment”, IEEE Signal Processing Letters, vol. 23, no. 1, pp. 55-59, 2016, doi: 10.1109/LSP.2015.2499778.
  • [24] B. Wang and Y. Gao, “A Novel Line Integral Transform for 2D Affine-Invariant Shape Retrieval”, in Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020. Springer, 2020, pp. 596-611, doi: 10.1007/978-3-030-58604-1_36.
  • [25] X. Xiong and K. Qin, “Linearly estimating all parameters of affine motion using radon transform”, IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4311-4321, 2014.
  • [26] Y.-Z. Huang, Two-dimensional conformal geometry and vertex operator algebras. Springer Science & Business Media, 1997.
  • [27] I.H. Mohammed, T.N. Ataiwe, and H. Al Sharaa, “Estimation the components of deflection of the vertical for a point in Baghdad City-Iraq”, Journal of Physics: Conference Series, vol. 1895, no. 1, art. no. 012005, 2021, doi: 10.1088/1742-6596/1895/1/012005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-64de5463-97d6-4cc0-95bf-1bfdb6dbdc28
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