Robotics and Computer Vision Lab

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International Conference
2014.09.03 20:31

Robust Binary Feature Using Intensity Order

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저 자 Yukyung Choi, Chaehoon Park, Joon-Young Lee, In So Kweon
학 회 The 12th Asian Conference on Computer Vision (ACCV)
Notes The first and the second authors provided equal contributions to this work. (Acceptance rate 27%)
논문일시(Year) 2014
논문일시(Month) 11
https://sites.google.com/site/ykchoicv/feature_bioBinary features have received much attention for memory and computational efficiency as emerging demands of mobile and embedded vision systems. In this context, we present a robust binary feature using intensity order. By analyzing feature regions, we take a simple but effective strategy to detect keypoint. We adopt ordinal description and encode intensity order into binary descriptor with a proper binarization. As a result, our method obtains high repeatability and shows better performance on feature matching with much less storage usage than other conventional features. We evaluate the performance of the proposed binary feature with various experiments, demonstrate its efficiency in terms of storage and computation time, and show the robustness under various geometric and photometric transformations.



This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (No. 2010-0028680).

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» Robust Binary Feature Using Intensity Order
Yukyung Choi, Chaehoon Park, Joon-Young Lee, In So Kweon
The 12th Asian Conference on Computer Vision (ACCV) 2014 / 11
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The 12th Asian Conference on Computer Vision (ACCV) 2014 / 11
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