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http://dx.doi.org/10.5909/JBE.2018.23.4.559

Parameter Analysis for Time Reduction in Extracting SIFT Keypoints in the Aspect of Image Stitching  

Moon, Won-Jun (Department of Electronic Materials Engineering, Kwangwoon University)
Seo, Young-Ho (Ingenium College of Liberal Arts, Kwangwoon University)
Kim, Dong-Wook (Department of Electronic Materials Engineering, Kwangwoon University)
Publication Information
Journal of Broadcast Engineering / v.23, no.4, 2018 , pp. 559-573 More about this Journal
Abstract
Recently, one of the most actively applied image media in the most fields such as virtual reality (VR) is omni-directional or panorama image. This image is generated by stitching images obtained by various methods. In this process, it takes the most time to extract keypoints necessary for stitching. In this paper, we analyze the parameters involved in the extraction of SIFT keypoints with the aim of reducing the computation time for extracting the most widely used SIFT keypoints. The parameters considered in this paper are the initial standard deviation of the Gaussian kernel used for Gaussian filtering, the number of gaussian difference image sets for extracting local extrema, and the number of octaves. As the SIFT algorithm, the Lowe scheme, the originally proposed one, and the Hess scheme which is a convolution cascade scheme, are considered. First, the effect of each parameter value on the computation time is analyzed, and the effect of each parameter on the stitching performance is analyzed by performing actual stitching experiments. Finally, based on the results of the two analyses, we extract parameter value set that minimize computation time without degrading.
Keywords
omni-directional image; image stitching; SIFT keypoints; SIFT parameters; Gaussian pyramid;
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1 K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.10, pp.1615-1630, Oct. 2004, doi: 10.1109/TPAMI.2005.188.   DOI
2 http://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15463-f10/www/proj4/www/junjieli/
3 http://www.cs.cmu.edu/afs/andrew/scs/cs/15-463/ f07/proj4/www/lisachan/
4 http://hugin.sourceforge.net/tutorials/two-photos/en.shtml
5 https://sites.google.com/a/umich.edu/eecs442-winter2015/home- work/image-stitching
6 http://www.cad.zju.edu.cn/home/gfzhang/training/Panorama/Panorama.htm
7 https://courses.engr.illinois.edu/cs498dwh/fa2010/lectures/Lecture%2017%20-%20Photo%20Stitching.pdf
8 M. Grabner, H, Grabner, and H. Bischof, "Fast approximated SIFT," Asian Conference on Computer Vision, pp.918-927, 2006, https://doi.org/10.1007/11612032_92.   DOI
9 Y. Ke, and R. Sukthankar, "PCA-SIFT: A more distinctive representation for local image descriptors," IEEE CVPR2004, Washington DC, USA, pp.506-513, July, 2004, doi: 10.1109/CVPR.2004.1315206.   DOI
10 H. Bay, T. Tuytelaars, and L. V. Gool "SURF: Speeded Up Robust Features," European Conference on Computer Vision, Graz, Austria, pp.404-417, 2006, https://doi.org/10.1007/11744023_32.   DOI
11 R. Hess, "An Open-Source SIFT Library," ACM Multimedia, Firenze, Italy, pp.1493-1496, Oct. 2010.
12 J. Qiu, T. Huang, and T. Ikenaga, "A FPGA-based dual-pixel processing pipelined hardware accelerator for feature point detection in SIFT," 5th International Joint Conference on INC, IMS and IDC, Seoul, South Korea, pp.1668-1674, Nov. 2009, doi: 10.1109/NCM.2009.38.   DOI
13 H. D. Chati, F. Muhlauer, T. Braum, C. Bobda, and K. Berns, "Hardware/software co-design of a key point detector on FPGA," 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, Napa, USA, pp.355-356, 2007, doi: 10.1109/FCCM.2007.61.   DOI
14 M. Lalonde, D. Bryns, L. Gargon, N. Teasdale, and D. Laurendeau, "Real-time eye blink detection with GPU-based SIFT tracking," 4th Canadian Conference on Computer and Robot Vision, Montreal, Canada, pp.481-487, 2007, doi: 10.1109/CRV.2007.54.   DOI
15 G. Hsu, C. Lin, and J. Wu, "Real-time 3-D object recognition using scale invariant feature transform and stereo vision," 4th International Conference on Autonomous Robots Agents, Wellington, New Zealand, pp.239-244, 2009, doi: 10.1109/ICARA.2000.4803919.   DOI
16 Institute for Information & communications Technology Promotion, Technology Development Trend and Market Forecast of VR/AR, Weekly Technology Trends, Vol.1803, July, 2017.
17 D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, Vol.60, No.2, pp.91-110, Jan. 2004, https://doi.org/10.1023/B:VISI.0000029664.99615.94.   DOI
18 M. Brown, and D. G. Lowe, "Automatic Panoramic Image Stitching using Invariant Features," International Journal of Computer Vision, Vol. 74, No.1, pp.59-73, Aug. 2007, https://doi.org/10.1007/s11263-006-0002-3.   DOI
19 F. C. Huang, S. Y. Huang, J. W. Ker, and Y. C. Chen "High- Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction," IEEE Transactions on Circuits and Systems for Video Technology, Vol.22, No.3, pp.340-351, Mar. 2012, doi: 10.1109/TCSVT.2011.2162760.   DOI