Abstract
Camera Calibration (CC) is required in many Photogrammetry and Computer-Vision applications, where 3D information is extracted from images and CC is also employed for pose determination of imaging sensors. In this paper, a novel implicit-CC model (ICC) based on Adaptive Neuro Fuzzy Inference System has been introduced. The ICC is particularly useful for back-projection in the applications that do not require internal and external camera calibration parameters in addition to the expert knowledge. The ICC supports multi-view back-projection in intelligent-photogrammetry. In this paper, the back-projection performance of the ICC has been compared with the Modified Direct-Linear-Transformation (MDLT) on real-images in order to evaluate the success of the proposed ICC. Extensive simulation results show that the ICC achieves a better performance than the MDLT in the 3D reconstruction of scene.
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Klir, G., Wang, Z., Harmanec, D.: Geometric Camera Calibration Using Circular Control Points. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1066–1076 (2000)
Pan, H.P.: A Basic Theory of Photogrammetron. International Archives of Photogrammetry and Remote Sensing XXXIV (3) (2002)
Pan, H.P., Zhang, C.S.: System Calibration of Intelligent Photogrammetron. International Archives of Photogrammetry and Remote Sensing XXXIV (2) (2002)
Lynch, M.B., Dagli, C.H., Vallenki, M.: The Use of Fedforward Neural Networks for Machine Vision Calibration. Int. Journal of Production Economics, 60–61, 479-489, (1999)
Hatze, H.: High-Precision Three-Dimensional Photogrammetric Calibration and Object Space Reconstruction Using a Modified Dlt-Approach. J. of Biomechanics 21, 533–538 (1988)
Ahmed, M.T., Hemayed, E., Farag, A.: Neurocalibration: A Neural Network that can Tell Camera Calibration Parameters. In: Proc. of the International Conference on Computer Vision, Korfu, Greece, vol. 1, pp. 463–468 (1999)
Jun, J., Choongwon, K.: Robust Camera Calibration Using Neural Network. In: IEEE Region 10 Conference TENCON 1999, vol. 1, pp. 1694-1697 (1999)
Lucchese, L.: Geometric Calibration of Digital Cameras through Multi-View Rectification. Image and Computing 23, 517–539 (2005)
Jang, J.S.R.: Anfis: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993)
Beşdok, E., Çiviciog̃lu, P., Alç\(\imath\), M.: Using an Adaptive Neuro-Fuzzy Inference System-Based Interpolant for Impulsive Noise Suppression from Highly Distorted Images. Fuzzy Sets and Systems 150, 525–543 (2005)
Beşdok, E., Çiviciog̃lu, P., Alç\(\imath\), M.: Using Anfis with Circular Polygons for Impulsive Noise Suppression From Highly Distorted Images, AEU-International Journal of Electronics and Communications 59 (4), 213-221 (2005)
Heping, P., Chusen, Z.: System Structure and Calibration Models of Intelligent Photogrammetron. Wuhan University Journal 2(48) (2003)
FarField Technology, FastRBF Toolbox, MATLAB Interface Version 1.4 (2004), http://www.farfieldtechnology.com/products/toolbox/
Mathworks Inc., Matlab Neural Networks Toolbox and Fuzzy Toolbox, Mathworks (2005)
Abdel-Aziz, Y.I., Karara, H.M.: Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry. In: Proceedings of the Symposium on Close-Range Photogrammetry, pp. 1–18. American Society of Photogrammetry, Falls Church (1971)
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© 2006 Springer-Verlag Berlin Heidelberg
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Beṣdok, E., Çivicioğlu, P. (2006). Adaptive Implicit-Camera Calibration in Photogrammetry Using Anfis. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_73
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DOI: https://doi.org/10.1007/11892960_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
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