Robotics and Computer Vision Lab

Publications

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저 자 Hae-Gon Jeon, Joon-Young Lee, Yudeog Han, Seon Joo Kim, In So Kweon
학 회 IEEE International Conference on Computer Vision (ICCV)
Notes This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MEST) (No.2010- 0028680).
BidTex Ref @inproceedings{jeon:iccv13, AUTHOR = {Hae-Gon Jeon and Joon-Young Lee and Yudeog Han and Seon Joo Kim and In So Kweon}, TITLE = {Fluttering Pattern Generation using Modified Legendre Sequence for Coded Exposure Imaging}, BOOKTITLE = {Proceedings of IEEE International Conference on Computer Vision (ICCV)}, YEAR = {2013} }
논문일시(Year) 2013
논문일시(Month) 12
https://sites.google.com/site/jyleecv/legendre_coded_exposureFinding a good binary sequence is critical in determining the performance of the coded exposure imaging, but previous methods mostly rely on a random search for finding the binary codes, which could easily fail to find good long sequences due to the exponentially growing search space. In this paper, we present a new computationally efficient algorithm for generating the binary sequence, which is especially well suited for longer sequences. We show that the concept of the low autocorrelation binary sequence that has been well exploited in the information theory community can be applied for generating the fluttering patterns of the shutter, propose a new measure of a good binary sequence, and present a new algorithm by modifying the Legendre sequence for the coded exposure imaging. Experiments using both synthetic and real data show that our new algorithm consistently generates better binary sequences for the coded exposure problem, yielding better deblurring and resolution enhancement results compared to the previous methods for generating the binary codes.

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