Subscribe Now Subscribe Today
Research Article

Fast Correction Algorithm Research of Image Geometric Distortion in the Image Tracking

Tan Lian, Dang Pei , Luo Qiang and Mutasem Alsmadi
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

In high-performance image tracking system, the image geometric distortion is an important factor restricting accuracy of the algorithm. In order to ensure accuracy and to minimize the computation time, this study proposes a method through research. Radial distortion is the main factor of the image distortion. By the coordinate transformation, this paper got the camera distortion model. This study briefly introduces the image of the principles of geometric distortions. And in the basis of analyzing polynomial algorithm on the coordinate conversion, it approaches a distortion algorithm non-uniform by region. The fixed focus image is established to the corresponding model and non-uniform divided to rectangular areas which are within a polynomial in a high order polynomial. By comparing the correction effect and devotion of time of the correction method between one order region and third order polynomial, the validity of the proposed method by the article can be verified.

Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

  How to cite this article:

Tan Lian, Dang Pei , Luo Qiang and Mutasem Alsmadi, 2013. Fast Correction Algorithm Research of Image Geometric Distortion in the Image Tracking. Journal of Applied Sciences, 13: 2876-2883.

DOI: 10.3923/jas.2013.2876.2883


Ai, L., F. Yuan and Z. Ding, 2008. Further study on radial distortion model for photographic objective. Acta Optica Sinica, 28: 1930-1933.

Asari, K.V., S. Kumar and D. Radhakrishnan, 1999. A new approach for nonlinear distortion correction in endoscopic images based on least squares estimation. IEEE Trans. Med. Imaging, 18: 345-354.
CrossRef  |  Direct Link  |  

Hao, P., L. Fu, L. Yuan, W. Li and B. Pan, 2006. Compensating test of the reflective mirror. Acta Optica Sinica, 26: 831-835.

Hartley, R. and A. Zisserman, 2000. Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge, pp: 34-52.

Lee, S., J.M. Reinhardt, P.C. Cattin and M.D. Abramoff, 2010. Objective and expert-independent validation of retinal image registration algorithms by a projective imaging distortion model. Med. Image Anal., 14: 539-549.
CrossRef  |  Direct Link  |  

Liao, S.Z., P.H. Gao, Y. Su and D.P. Wang, 2000. A geometric rectification method for lens camera. J. Image Graphics, 5: 593-596.

Luong, Q.T. and O. Faugeras, 1996. The fundamental matrix: Theory, algorithms and stability analysis. Int. J. Comput. Vis., 17: 43-75.
Direct Link  |  

Mezaris, V., I. Kompatsiaris, N.V. Boulgouris and M.G. Strintzis, 2004. Real-time compressed-domain spatiotemporal segmentation and ontologies for video indexing and retrieval. IEEE Trans. Circuits Syst. Video Technol., 14: 606-621.
CrossRef  |  Direct Link  |  

Nicolescu, C. and P. Jonker, 2002. A data and task parallel image processing environment. Parallel Comput., 28: 945-965.
CrossRef  |  Direct Link  |  

Nomura, Y., M. Sagara, H. Naruse and A. Ide, 1992. Simple calibration algorithm for high-distortion lens camera. IEEE Trans. Pattern Anal. Mach. Intell., 14: 1095-1099.
CrossRef  |  Direct Link  |  

Ren, H.H., P. Ruan, J.W. He, W.D. Qiao, S.T. Liang and W. Wei, 2010. Study of the radiation calibration of TDI-CCD spatial stereo camera. Acta Optica Sinica, 30: 3476-3481.

Shah, S. and J.K. Aggarwal, 1996. Intrinsic parameter calibration procedure for a (high-distortion) fish-eye lens camera with distortion model and accuracy estimation. Pattern Recognit., 29: 1775-1788.
CrossRef  |  Direct Link  |  

Xue, L., X. Rao, C. Wang, Y. Hu, N. Ling and W. Jiang, 2007. Higher-order aberrations correction and vision analysis system for human eye. Acta Optica Sinica, 27: 893-897.

©  2020 Science Alert. All Rights Reserved