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Xin Yang , University of California, Santa Barbara, Santa Barbara
Kwang-Ting Tim Cheng , University of California, Santa Barbara, Santa Barbara
ABSTRACT
The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile Augmented Reality system. However, existing descriptors are either too computationally expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly efficient and distinctive binary descriptor, called Learning-based Local Difference Binary (LLDB). LLDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones. Experimental results demonstrate that LLDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Comparing to existing binary descriptors, LLDB has similar efficiency for descriptor construction, while achieves a greater accuracy and faster matching speed when matching over a large database on mobile devices.
INDEX TERMS
Computer vision, Image Representation, Feature representation, Object recognition
CITATION
Xin Yang, Kwang-Ting Tim Cheng, "Learning Optimized Local Difference Binaries for Scalable Augmented Reality on Mobile Devices", IEEE Transactions on Visualization & Computer Graphics, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TVCG.2013.260
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