CONTROL OF ENERGY EFFICIENCY IN INDUSTRY AND HOUSING AND COMMUNAL SERVICES
Sign | Register
UDC 004.021
The analysis of performance and computing complexity of 3D-reconstraction algorithms in terms of their applicability on low power consumption processors
Alexander Vyacheslavovich Argutin, post-graduate student of Electronic Computers Department, South Ural State University, Chelyabinsk, Russian Federation, alex.argutin@gmail.com
Abstract
3D-reconstraction algorithms and their applicability for computer vision problems solution on low power consumption processors are considered. The analysis of performance and computing complexity of 3D-reconstraction algorithms allows to determine their preferences and to make optimal choice depending on implementation conditions.
Keywords
computer vision, 3D-reconstraction, algorithms
References
1. The Middlebury Stereo Vision Page, an evaluation of dense two-frame stereo algorithms. – http://vision.middlebury.edu/stereo/eval/.
2. Hirschmüller, H. Evaluation of Stereo Matching Costs on Images with Radiometric Differences / H. Hirschmüller, D. Scharstein // IEEE Transactions on pattern analysis and machine intelligence. – 2008. – V. 31, № 9. – P. 1582–1599.
3. Hirschmüller, H., Evaluation of Cost Functions for Stereo Matching / H. Hirschmüller, D. Scharstein // IEEE Conference on Computer Vision and Pattern Recognition. – 2007. – V. 1. – P. 1–8.
4. Hirschmüller, H., Accurate and Effcient Stereo Processing by Semi-Global Matching and Mutual Information / H. Hirschmüller // IEEE Conference on Computer Vision and Pattern Recognition. – 2005. – V. 2. – P. 807–814.
5. Cevahir, C. Efficient Edge-Preserving Stereo Matching / C. Cevahir, A.A. Alatan // IEEE International Conference on Computer Vision Workshops ICCV 2011 Workshops, Barcelona, Spain, November 6–13, 2011. – 2011. – P. 696–699.
6. Optimized abs function. – http://www.strchr.com/optimized_abs_function
7. Ambrosch, K. Accurate hardware-based stereo vision / K. Ambrosch, W. Kubinger // Computer Vision and Image Understanding. – 2010. – V. 114, № 11. – P. 1303–1316.
8. Baik, Y.K., Fast Census Transform-based Stereo Algorithm using SSE2 / Y.K. Baik, J.H. Jo, K.M. Lee // The 12th Korea Japan Joint Workshop on Frontiers of Computer Vision, Seoul National University Computer Vision Lab., Seoul, Feb. – 2006. – 2006. – P. 305–309.
9. Blejhut, R. Teoriya i praktika kodov, kontroliruyushchih oshibki / R. Blejhut. – M.: Izd-vo «Mir», 1986. – 576 s.
10. The popcount algorithm. – http://wiki.cs.pdx.edu/forge/popcount.html
11. Birchfield, S. A pixel dissimilarity measure that is insensitive to image sampling / S. Birchfield, C. Tomasi // TPAMI. – 1998. – V. 20, № 4. – P. 401–406.
12. Zabih, R. Non-parametric Local Transforms for Computing Visual Correspondence / R. Zabih, W. John // Third European Conference on Computer Vision, Stockholm, Sweden, May 1994. – 1994. – P. 151–158.
2. Hirschmüller, H. Evaluation of Stereo Matching Costs on Images with Radiometric Differences / H. Hirschmüller, D. Scharstein // IEEE Transactions on pattern analysis and machine intelligence. – 2008. – V. 31, № 9. – P. 1582–1599.
3. Hirschmüller, H., Evaluation of Cost Functions for Stereo Matching / H. Hirschmüller, D. Scharstein // IEEE Conference on Computer Vision and Pattern Recognition. – 2007. – V. 1. – P. 1–8.
4. Hirschmüller, H., Accurate and Effcient Stereo Processing by Semi-Global Matching and Mutual Information / H. Hirschmüller // IEEE Conference on Computer Vision and Pattern Recognition. – 2005. – V. 2. – P. 807–814.
5. Cevahir, C. Efficient Edge-Preserving Stereo Matching / C. Cevahir, A.A. Alatan // IEEE International Conference on Computer Vision Workshops ICCV 2011 Workshops, Barcelona, Spain, November 6–13, 2011. – 2011. – P. 696–699.
6. Optimized abs function. – http://www.strchr.com/optimized_abs_function
7. Ambrosch, K. Accurate hardware-based stereo vision / K. Ambrosch, W. Kubinger // Computer Vision and Image Understanding. – 2010. – V. 114, № 11. – P. 1303–1316.
8. Baik, Y.K., Fast Census Transform-based Stereo Algorithm using SSE2 / Y.K. Baik, J.H. Jo, K.M. Lee // The 12th Korea Japan Joint Workshop on Frontiers of Computer Vision, Seoul National University Computer Vision Lab., Seoul, Feb. – 2006. – 2006. – P. 305–309.
9. Blejhut, R. Teoriya i praktika kodov, kontroliruyushchih oshibki / R. Blejhut. – M.: Izd-vo «Mir», 1986. – 576 s.
10. The popcount algorithm. – http://wiki.cs.pdx.edu/forge/popcount.html
11. Birchfield, S. A pixel dissimilarity measure that is insensitive to image sampling / S. Birchfield, C. Tomasi // TPAMI. – 1998. – V. 20, № 4. – P. 401–406.
12. Zabih, R. Non-parametric Local Transforms for Computing Visual Correspondence / R. Zabih, W. John // Third European Conference on Computer Vision, Stockholm, Sweden, May 1994. – 1994. – P. 151–158.
Source
Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control, Radio Electronics, 2012, iss. 16, no. 23 (282), pp. 213-215. (in Russ.) (Brief Reports)