GPU implementation of a clustering based image registration

Seung Hun Yoo; Yun Seok Lee; Sung Up Jo; Chang Sung Jeong

doi:10.1007/978-3-540-87442-3_61

GPU implementation of a clustering based image registration

Seung Hun Yoo, Yun Seok Lee, Sung Up Jo, Chang Sung Jeong

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper presents GPU(Graphics Processing Unit) implementation of a clustering based image registration method. Since the image registration is an important process in image analysis tasks such as image restoration and image fusion, fast image registration can improve the overall application execution speed. Recently, the commodity GPU is being used in not only 3D graphics rendering but also in general-purpose computation due to an increase in the good price/performance ratio and hardware programmability as well as the huge computing power and speed of the GPU. We implemented clustering-based image registration method on GPU using only transformation of texture coordinations in vertex program and re-sampling in fragment program. Finally, GPU-based image registration speed up nearly 50 percent compared with CPU.

Original language	English
Title of host publication	Advanced Intelligent Computing Theories and Applications
Subtitle of host publication	With Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings
Pages	491-497
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-540-87442-3_61
Publication status	Published - 2008
Event	4th International Conference on Intelligent Computing, ICIC 2008 - Shanghai, China Duration: 2008 Sept 15 → 2008 Sept 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5226 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	4th International Conference on Intelligent Computing, ICIC 2008
Country/Territory	China
City	Shanghai
Period	08/9/15 → 08/9/18

Keywords

Clustering
GPU
Image registration

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-87442-3_61

Cite this

Yoo, S. H., Lee, Y. S., Jo, S. U., & Jeong, C. S. (2008). GPU implementation of a clustering based image registration. In Advanced Intelligent Computing Theories and Applications: With Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings (pp. 491-497). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5226 LNCS). https://doi.org/10.1007/978-3-540-87442-3_61

GPU implementation of a clustering based image registration. / Yoo, Seung Hun; Lee, Yun Seok; Jo, Sung Up et al.
Advanced Intelligent Computing Theories and Applications: With Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. p. 491-497 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5226 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yoo, SH, Lee, YS, Jo, SU & Jeong, CS 2008, GPU implementation of a clustering based image registration. in Advanced Intelligent Computing Theories and Applications: With Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5226 LNCS, pp. 491-497, 4th International Conference on Intelligent Computing, ICIC 2008, Shanghai, China, 08/9/15. https://doi.org/10.1007/978-3-540-87442-3_61

Yoo SH, Lee YS, Jo SU, Jeong CS. GPU implementation of a clustering based image registration. In Advanced Intelligent Computing Theories and Applications: With Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. p. 491-497. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-87442-3_61

Yoo, Seung Hun ; Lee, Yun Seok ; Jo, Sung Up et al. / GPU implementation of a clustering based image registration. Advanced Intelligent Computing Theories and Applications: With Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. pp. 491-497 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "GPU implementation of a clustering based image registration",

abstract = "This paper presents GPU(Graphics Processing Unit) implementation of a clustering based image registration method. Since the image registration is an important process in image analysis tasks such as image restoration and image fusion, fast image registration can improve the overall application execution speed. Recently, the commodity GPU is being used in not only 3D graphics rendering but also in general-purpose computation due to an increase in the good price/performance ratio and hardware programmability as well as the huge computing power and speed of the GPU. We implemented clustering-based image registration method on GPU using only transformation of texture coordinations in vertex program and re-sampling in fragment program. Finally, GPU-based image registration speed up nearly 50 percent compared with CPU.",

keywords = "Clustering, GPU, Image registration",

author = "Yoo, {Seung Hun} and Lee, {Yun Seok} and Jo, {Sung Up} and Jeong, {Chang Sung}",

note = "Funding Information: This research was supported by the Seoul R&BD Program, a program of MIC (Ministry of Information and Communication) of Korea, the ITRC(Information Technology Research Center) support program of IITA (Institute of Information Technology Assessment), a Brain Korea 21 project, Seoul Forum for Industry-University-Research Cooperation and a grant of Korea University.; 4th International Conference on Intelligent Computing, ICIC 2008 ; Conference date: 15-09-2008 Through 18-09-2008",

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AU - Jeong, Chang Sung

N1 - Funding Information: This research was supported by the Seoul R&BD Program, a program of MIC (Ministry of Information and Communication) of Korea, the ITRC(Information Technology Research Center) support program of IITA (Institute of Information Technology Assessment), a Brain Korea 21 project, Seoul Forum for Industry-University-Research Cooperation and a grant of Korea University.

PY - 2008

Y1 - 2008

N2 - This paper presents GPU(Graphics Processing Unit) implementation of a clustering based image registration method. Since the image registration is an important process in image analysis tasks such as image restoration and image fusion, fast image registration can improve the overall application execution speed. Recently, the commodity GPU is being used in not only 3D graphics rendering but also in general-purpose computation due to an increase in the good price/performance ratio and hardware programmability as well as the huge computing power and speed of the GPU. We implemented clustering-based image registration method on GPU using only transformation of texture coordinations in vertex program and re-sampling in fragment program. Finally, GPU-based image registration speed up nearly 50 percent compared with CPU.

AB - This paper presents GPU(Graphics Processing Unit) implementation of a clustering based image registration method. Since the image registration is an important process in image analysis tasks such as image restoration and image fusion, fast image registration can improve the overall application execution speed. Recently, the commodity GPU is being used in not only 3D graphics rendering but also in general-purpose computation due to an increase in the good price/performance ratio and hardware programmability as well as the huge computing power and speed of the GPU. We implemented clustering-based image registration method on GPU using only transformation of texture coordinations in vertex program and re-sampling in fragment program. Finally, GPU-based image registration speed up nearly 50 percent compared with CPU.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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GPU implementation of a clustering based image registration

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