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Performance Optimization of LLAH for Tracking Random Dots under Gaussian Noise
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  • Journal title : Journal of Broadcast Engineering
  • Volume 20, Issue 6,  2015, pp.912-920
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2015.20.6.912
 Title & Authors
Performance Optimization of LLAH for Tracking Random Dots under Gaussian Noise
Park, Hanhoon;
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Unlike general texture-based feature description algorithms, Locally Likely Arrangement Hashing (LLAH) algorithm describes a feature based on the geometric relationship between its neighbors. Thus, even in poor-textured scenes or large camera pose changes, it can successfully describe and track features and enables to implement augmented reality. This paper aims to optimize the performance of LLAH algorithm for tracking random dots (= features) with Gaussian noise. For this purpose, images with different number of features and magnitude of Gaussian noise are prepared. Then, the performance of LLAH algorithm according to the conditions: the number of neighbors, the type of geometric invariants, and the distance between features, is analyzed, and the optimal conditions are determined. With the optimal conditions, each feature could be matched and tracked in real-time with a matching rate of more than 80%.
LLAH;random dot tracking;Gaussian noise;feature description;augmented reality;
 Cited by
Performance Analysis of Brightness-Combined LLAH, Journal of Korea Multimedia Society, 2016, 19, 2, 138  crossref(new windwow)
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