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Using graphical models for depth map estimation

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Języki publikacji
EN
Abstrakty
EN
Two slightly different projections of the same scene allow the stereovision algorithms to reconstruct its 3D structure and to estimate the distance to particular object. However commonly used real-time correlation-based solutions usually suffer from inaccuracy. Therefore, finding an efficient and accurate algorithm for depth reconstruction is still a challenging task to do. The approach to stereo matching, presented in this paper is described as a problem of correlating different local observations that evaluate the dissimilarities between left and right images in order to obtain smooth and more accurate depth map. The results obtained with the proposed method are evaluated and compared with other state of the art methods.
Twórcy
autor
autor
  • Institute of Telecommunications, University of Technology & Life Sciences, Kaliskiego 7, 85-796 Bydgoszcz, Poland, rafal@utp.edu.pl
Bibliografia
  • [1] Learning Depth from Single Monocular Images, Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng. In Neural Information Processing Systems (NIPS) 18, 2005.
  • [2] Needleman, S. B. and Wunsch, C. D. A general method applicable to the search for similarities In the amino acid sequence of two proteins. Journal of Molecular Biology, 1970
  • [3] S. Birchfield and C. Tomasi. Depth Discontinuities by Pixel-to-Pixel Stereo. International Journal of Computer Vision, 35(3): 269-293, December 1999.
  • [4] Make3D Project http://make3d.cs.Cornell.edu/
  • [5] Daniel P. Huttenlocher Pedro F. Felzenszwalb. Efficient Belief Propagation for Early Vision. International Journal of Computer Vision. 2006.
  • [6] Pawel Pelczynski, „Travel Aid System for the Blind”, Image Processing and Communications Challenges, p. 324-333, 2009
  • [7] http://www.gdp-research.com.au The Miniguide project homepage.
  • [8] J.Sun, Y.Li, S Kang, and H. Shum. Symmetric stereo matching for occlusion handling, In CVPR, pages II: 399-406, 2005.
  • [9] V.Kolmogorov and R.Zabih. Computing visual correspondence with occlusions via graph cuts. In I, pages II: 508-515, 2001.
  • [10] Sivic, J. and Zisserman, A. Efficient Visual Search for Objects in Videos. Proceedings of the IEEE (2008)
  • [11] Damir Kirasic and Danko Basch. Ontology-Based Design Pattern Recognition. Lecture Notes in Computer Science (2009).
  • [12] Francois, A.R.J., Nevatia, R., Hobbs, J., Bolles, R.C. VERL: an ontology framework for representing and annotating video events. IEEE MultiMedia, Vol. 12 No. 4, pp. 76-86 (2005).
  • [13] Latfi, F., Lefebvre, B., Descheneaux, C. Ontology-based management of the telehealth smart home, dedicated to elderly in loss of cognitive autonomy. CEUR Workshop Proceedings, Vol. 258 (2007)
  • [14] Torralba, R. Fergus, and Y. Weiss. Small codes and large databases for recognition. In CVPR, 2008.
  • [15] Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: CVPR. (2008)
  • [16] P.F. Felzenszwalb and D.P. Huttenlocher. Efficient belief propagation for early vision. In CVPR, pages 1:261-268, 2004
  • [17] S.B.Needleman and C.D. Wunsch. A general method applicable to the search for similarities in the aminoacid sequence of two proteins. J. Mol. Biol. 1970.
  • [18] Depth Estimation using Monocular and Stereo Cues, Ashutosh Saxena, Jamie Schulte, Andrew Y. Ng. In IJCAI2007.
  • [19] 3-D Depth Reconstruction from a Single Still Image, Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng. In IJCV 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0073-0016
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