Abstract
Based on hardware architecture of CUDA (Compute Unified Device Architecture), this paper not only makes full use of multithreaded and parallelism in GPU (Graphic Processing Unit, the image Processing Unit), but also takes advantage of the memory to improve parallelization of SIFT algorithm. What’s more, two-dimensional thread structure is adopted when storing the image data and variable blockIdx which is built in the device is used for mapping width and height in pixels of the two-dimensional images. Thus, it can improve the efficiency of parallel by making full use of thread parallelism and two-dimensional features of thread grid. The experimental results show that the matching accuracy and speed of the algorithm have greatly improved compared to the traditional serial SIFT algorithm, and the maximum acceleration ratio can reach 21.43 in this experiment making image parallel matching possible under the smart vehicle environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lowe, D.G.: Object recognition from local scale-invariant features. In:7th IEEE International Conference on Computer Vision (ICCV), Greece, pp.1150–1157 (1999)
Lowe, D.G.:Distictive image features from scale-invariant keypoints. 60(2), 9-110 (2004)
Sinha, S.,Frahm, J.M., Pollefeys, M.: Feature tracking and matching in video using programmable graphics hardware (2012)
Zhang, Q., Chen, Y.R., Zhang, Y.M.: SIFT implementation and optimization for multi-core systems. In: IEEE International Symposium on Parallel and Distributed Processing, pp: 1-8. IEEE Press, Miami (2008)
Wang, R., Liang, H., Cai, X.: Study of SIFT feature extraction algorithm based on GPU. Modern Electronic Technique (2010)
Hua, N.: GPU general computation and study of parallel computing of image registration based on SIFT feature. Xidian University, Xi’an (2010)
Wang, B.L., Zhu, Z.L., Meng, L.: SIFT feature extraction based on CUDA acceleration. Northeastern university, ShenYang (2013). (in Chinese)
Zuo,H.R., Zhang, Q.H., Xu, Y.: Fast Sobel edgedetection algorithm based on GPU. Opto-Electronic Engineering (2009)
Zhang, H., Gu, X.F.: Research and implementation of image automatic fast splicing in unmanned aerial vehicle. University of electronic science and technology, Beijing (2012). (2009)
Ke, Y., Sukthankar, R.: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors (2004)
Farrugia, J.P., Horain, P., Guehenneux, E., etc.: GPUCV: A framework for image processing acceleration with graphics processors. In: Proceedings of 2006 IEEE International Conference on Multimedia and Expo. Canada., pp. 585–588 (2006)
NVIDIA CUDA Compute Unified Device Architecture programming guide,version2.0
Schmid, C., Mohr, R.: Local gray value invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 336–410 (1997). CUDA Best Practices Guide, version3.0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, HQ., Li, Yy., Li, Tt. (2015). A Parallel SIFT Algorithm for Image Matching Under Intelligent Vehicle Environment. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Di, K. (eds) Advances in Image and Graphics Technologies. IGTA 2015. Communications in Computer and Information Science, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47791-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-662-47791-5_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47790-8
Online ISBN: 978-3-662-47791-5
eBook Packages: Computer ScienceComputer Science (R0)