METHOD OF ENVIRONMENT RECONSTRUCTION USING EPIPOLAR IMAGES

Authors

  • Vasyl Koval
  • Oleh Adamiv
  • Anatoly Sachenko
  • Viktor Kapura
  • Hubert Roth

DOI:

https://doi.org/10.47839/ijc.11.1.545

Keywords:

3D Environment Reconstruction, Stereovision, Computer Vision, Epipolar Images.

Abstract

In this paper the method for 3D Environment reconstruction usingepipolar images is presented. Method allows to fuse stereoimages within certain time depending on acceptable computational resources

References

Ayache N., Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception, The MIT Press; Cambridge Massachusetts, London England. – 1991.

Hartley R., Zisserman A., Multiple-View Geometry in Computer Vision, 2nd edition; Cambridge university press. – 2003.

Marr D., Poggio T., A computational theory of human stereo vision, Proc. R. Soc. Lond., London, (1979), pp. 301-328.

Kanazawa Y., Uemura K., Wide baseline matching using triplet vector descriptor, Proc. 17th British Machine Vision Conf., Edinburgh, U.K., (I) (2006), pp. 267-276.

Ming J., Xianlin H., A lunar terrain reconstruction method using long base-line stereo vision, Proc. of the 26th Chinese Control Conference, Hunan, China, (2007), pp. 488-492.

Sekhavat S., Kamangar F., Geometric feature-based matching in stereo images, Proceedings of the Information, Decision and Control. – 1999.

Folsom T.C., Non-pixel robot stereo, Proc. of the IEEE Symposium on Computational Intelligence and Signal Processing (CIISP 2007). – 2007.

Moallem P., Fast edge-based stereo matching algorithm based on search space reduction, IEICE Transaction Information and Systems, Madrid, Spain E-85) 11 (2006), pp. 243-247.

Chen T., Klarquist W., Bovik A., Stereo vision using Gabor wavelets, Proc. IEEE International Conf. on Systems, Man, and Cybernetics, (1994), pp. 55-60.

Chen T., Bovik A., Super B., Multiscale stereopsis via Gabor filter phase response. – 1994.

Sarkar I., Bansal M., A wavelet-based multiresolution approach to solve the stereo correspondence problem using mutual information, IEEE transaction on systems, man. and cybernetics, (37) 4 (2007).

Yi J., Oh J., Recursive resolving algorithm for multiple stereo and motion matches, Image and Vision Computing, Vol. 15.-1997, pp. 181-196.

Torr P., Criminisi A., Dense stereo using pivoted dynamic programming, Image and Vision Computing, (22) (2004), pp. 795-806.

Maciel J., Costeira J., Robust point correspondence by concave minimization, Image and Vision Computing, (20) (2002), pp. 683-690.

Zhang Y., Gerbrands J., Method for matching general stereo planar curves, Image and Vision Computing, (13) (1995).

Shi J., Tomasi C., Good features to track, IEEE Conference on Computer Vision and Pattern Recognition, Seattle, 1994.

Wang J., Hsiao C., Stereo matching by neural network that uses sobel feature data, IEEE International Conference on Neural Networks, vol. 3, 3-6 June 1996, pp. 1801-1806.

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Published

2014-08-01

How to Cite

Koval, V., Adamiv, O., Sachenko, A., Kapura, V., & Roth, H. (2014). METHOD OF ENVIRONMENT RECONSTRUCTION USING EPIPOLAR IMAGES. International Journal of Computing, 11(1), 11-16. https://doi.org/10.47839/ijc.11.1.545

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Section

Articles