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Reseach Article

Distortion Correction using Enhanced Feature Extraction and Classification

by Varinderpal Singh, Surender Singh Saini, Jaget Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 131 - Number 14
Year of Publication: 2015
Authors: Varinderpal Singh, Surender Singh Saini, Jaget Singh
10.5120/ijca2015907538

Varinderpal Singh, Surender Singh Saini, Jaget Singh . Distortion Correction using Enhanced Feature Extraction and Classification. International Journal of Computer Applications. 131, 14 ( December 2015), 29-32. DOI=10.5120/ijca2015907538

@article{ 10.5120/ijca2015907538,
author = { Varinderpal Singh, Surender Singh Saini, Jaget Singh },
title = { Distortion Correction using Enhanced Feature Extraction and Classification },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 14 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number14/23519-2015907538/ },
doi = { 10.5120/ijca2015907538 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:27:22.903801+05:30
%A Varinderpal Singh
%A Surender Singh Saini
%A Jaget Singh
%T Distortion Correction using Enhanced Feature Extraction and Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 14
%P 29-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the advance in technology, different types of machines are used to acquire the images. These sensors acquire information and make it in the form of images. Sometime these images are affected by some distortion like barrel and pincushion. This is because theses sensors have a mark focus on either centre or edge. As it focuses on one point so it cannot be removed but can be corrected after acquiring samples. A number of methods have been used to correct this type of distortion. The previous work which we took under consideration was to collect information by extracting some texture features using that feature to classify the image which will provide the correct information at some sort of point. So, there is still need to improve the results because texture feature. But also edge feature and key point information using different algorithms. Then collect information by classifying them using neural classifier which achieves 93% accuracy.

References
  1. Byoung-Kwang Kim, Soon-Wook Chung, Moon-Kyu Song, Woo-Jin Song “Correcting Radial Lens Distortion with Advanced Outlier Elimination”, Domestic Journal, 2010.
  2. T. Thormählen and H. Broszio, “Automatic line-based estimation of radial lens distortion,” Integrated ComputerAided Engineering, vol. 12, no. 2, pp.177-190, 2005.
  3. R. Carroll, M. Agrawala and A. Agarwala, “Optimizing Content-Preserving Projections for Wide-Angle Images,” ACM transactions on Graphics, vol. 28, no. 3, article 43, Aug. 2009.
  4. A. Wang, T. Qiu and L. Shao, “A Simple Method of Radial Distortion Correction with Centre of Distortion Estimation,” Journal of Mathematical Imaging and Vision, vol. 35, no. 3, pp. 165–172, Jul. 2009.
  5. A. Nowakowski, and W. Skarbek, “Lens Radial Distortion Calibration Using Homography of Central Points,” EUROCON 2007. The International Conference “Computer as a Tool”, pp. 340-343, Sep. 2007.
  6. Ricolfe-Viala C, Sanchez-Salmeron A J, Valera A., "Efficient lens distortion correction for decoupling in calibration of wide angle lens cameras", IEEE Sensors Journal, 2013. vol.13, no.2, pp.854-863.
  7. Lee T Y, Wei C H, Lai S H, et al., "Real-time correction of wide-angle lens distortion for images with GPU computing", IEEE Asia Pacific Conference on Circuits and Systems, 2012, pp.456-459
  8. Lee T Y, Chang T S, Lai S H, et al., "Wide-angle distortion correction by Hough transform and gradient estimation". IEEE Visual Communications and Image Processing (VCIP), 2011, pp.1-4.
  9. R. Dong, B. Li, and Q.-M. Chen, “An automatic calibration method for PTZ camera in expressway monitoring system,” in Proc. World Congr. Comput. Sci. Inf. Eng., 2009, pp. 636–640.
  10. D. Dawson and S. Birchfield, “An energy minimization approach to auto- matic traffic camera calibration,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 3, pp. 1095–1108, Sep. 2013.
  11. L. Liu, J. Xing, G. Duan, and H. Ai, “Scene transformation for detector adaptation,” Pattern Recognit. Lett., vol. 36, pp. 154–160, Jan. 2013.
  12. Y. Zheng and S. Peng, “A practical roadside camera calibration method based on least squares optimization,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 831–843, Apr. 2014.
  13. N. K. Kanhere, S. T. Birchfield, and W. A. Sarasua, “Automatic cam- era calibration using pattern detection for vision-based speed sensing,” J. Transp. Res. Board, vol. 2086, no. 1, pp. 30–39, 2008.
  14. Z. Zhang, T. Tan, K. Huang, and Y. Wang, “Practical camera calibration from moving objects for traffic scene surveillance,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 3, pp. 518–533, Mar. 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Barrel Pincushion Neural Features.