ToF depth image motion blur detection using 3D blur shape models

Seungkyu Lee; Hyunjung Shim; James D.K. Kim; Chang Yeong Kim

doi:10.1117/12.908055

ToF depth image motion blur detection using 3D blur shape models

Seungkyu Lee, Hyunjung Shim, James D.K. Kim, Chang Yeong Kim

School of Integrated Technology

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

5 Citations (Scopus)

Abstract

Time-of-flight cameras produce 3D geometry enabling faster and easier 3D scene capturing. The depth camera, however, suffers from motion blurs when the movement from either camera or scene appears. Unlike other noises, depth motion blur is hard to eliminate by any general filtering methods and yields the serious distortion in 3D reconstruction, typically causing uneven object boundaries and blurs. In this paper, we provide a through analysis on the ToF depth motion blur and a modeling method which is used to detect a motion blur region from a depth image. We show that the proposed method correctly detects blur regions using the set of all possible motion artifact models.

Original language	English
Title of host publication	Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging X
DOIs	https://doi.org/10.1117/12.908055
Publication status	Published - 2012 Mar 13
Event	Computational Imaging X - Burlingame, CA, United States Duration: 2012 Jan 23 → 2012 Jan 24

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	8296
ISSN (Print)	0277-786X

Other

Other	Computational Imaging X
Country	United States
City	Burlingame, CA
Period	12/1/23 → 12/1/24

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All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Cite this

Lee, S., Shim, H., Kim, J. D. K., & Kim, C. Y. (2012). ToF depth image motion blur detection using 3D blur shape models. In Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging X [829615] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8296). https://doi.org/10.1117/12.908055

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abstract = "Time-of-flight cameras produce 3D geometry enabling faster and easier 3D scene capturing. The depth camera, however, suffers from motion blurs when the movement from either camera or scene appears. Unlike other noises, depth motion blur is hard to eliminate by any general filtering methods and yields the serious distortion in 3D reconstruction, typically causing uneven object boundaries and blurs. In this paper, we provide a through analysis on the ToF depth motion blur and a modeling method which is used to detect a motion blur region from a depth image. We show that the proposed method correctly detects blur regions using the set of all possible motion artifact models.",

author = "Seungkyu Lee and Hyunjung Shim and Kim, {James D.K.} and Kim, {Chang Yeong}",

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Lee, S, Shim, H, Kim, JDK & Kim, CY 2012, ToF depth image motion blur detection using 3D blur shape models. in Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging X., 829615, Proceedings of SPIE - The International Society for Optical Engineering, vol. 8296, Computational Imaging X, Burlingame, CA, United States, 12/1/23. https://doi.org/10.1117/12.908055

ToF depth image motion blur detection using 3D blur shape models. / Lee, Seungkyu; Shim, Hyunjung; Kim, James D.K.; Kim, Chang Yeong.

Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging X. 2012. 829615 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8296).

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

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AB - Time-of-flight cameras produce 3D geometry enabling faster and easier 3D scene capturing. The depth camera, however, suffers from motion blurs when the movement from either camera or scene appears. Unlike other noises, depth motion blur is hard to eliminate by any general filtering methods and yields the serious distortion in 3D reconstruction, typically causing uneven object boundaries and blurs. In this paper, we provide a through analysis on the ToF depth motion blur and a modeling method which is used to detect a motion blur region from a depth image. We show that the proposed method correctly detects blur regions using the set of all possible motion artifact models.

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Access to Document

10.1117/12.908055