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Award Abstract #1321168

CGV: Small: A Patch-based Framework for Capturing a World in Motion

NSF Org: IIS
Div Of Information & Intelligent Systems
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Initial Amendment Date: August 21, 2013
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Latest Amendment Date: July 16, 2015
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Award Number: 1321168
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Award Instrument: Continuing grant
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Program Manager: Jie Yang
IIS Div Of Information & Intelligent Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: October 1, 2013
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End Date: September 30, 2016 (Estimated)
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Awarded Amount to Date: $499,719.00
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Investigator(s): Pradeep Sen psen@ece.ucsb.edu (Principal Investigator)
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Sponsor: University of California-Santa Barbara
Office of Research
SANTA BARBARA, CA 93106-2050 (805)893-4188
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NSF Program(s): GRAPHICS & VISUALIZATION
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Program Reference Code(s): 7453, 7923
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Program Element Code(s): 7453

ABSTRACT

Although the real world is dynamic, many techniques used to image/capture it are fundamentally sequential in nature. For example, capturing a high-dynamic range (HDR) image of a scene (which contains a wide range of illumination) without special hardware involves taking a set of sequential images at different exposures, each one measuring a small range of illumination. However, this approach has problems when reconstructing the HDR image of dynamic scenes with moving subjects, since the individual frames are not registered correctly. Problems like this appear in many research areas, from medical imaging to computer vision.

In this project, the PI and his team are developing a common framework that addresses artifacts from motion for a variety of different applications. Their key insight is that patch-based optimization can be used to handle motion inconsistencies without explicitly solving the challenging problem of accurate motion estimation. This produces results that are reconstructed from different inputs in a consistent manner without motion artifacts. The PI is exploring how this framework can be applied to several important research areas, from high-quality imaging to computer vision applications such as the reconstruction of dynamic scenes.

Improved capture of dynamic scenes has the potential to transform the way certain imaging procedures (such as medical imaging) are done. Scientific results of this work are disseminated through technical publications at top venues in the graphics/vision communities, and the PI plans to release the algorithms developed online. Finally, this project involves high school and under-represented students into the research effort.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Kalantari, Nima Khademi and Shechtman, Eli and Barnes, Connelly and Darabi, Soheil and Goldman, Dan B. and Sen, Pradeep. "Patch-based High Dynamic Range Video," ACM Trans. Graph., v.32, 2013, p. 202:1--20. 

Matthias Zwicker, Wojciech Jarosz, Jaakko Lehtinen, Bochang Moon, Ravi Ramamoorthi, Fabrice Rousselle, Pradeep Sen, Cyril Soler, and Sung-Eui Yoon. "Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering," Computer Graphics Forum, v.34, 2015, p. 667. 

 

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