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Evaluation of Feature Extraction and Matching Algorithms for the use of Mobile Application  

Lee, Yong-Hwan (Dept. of Smart Mobile, Fat East University)
Kim, Heung-Jun (Dept. of Computer Science and Engineering, Gyeongnam National University of Science and Technology)
Publication Information
Journal of the Semiconductor & Display Technology / v.14, no.4, 2015 , pp. 56-60 More about this Journal
Abstract
Mobile devices like smartphones and tablets are becoming increasingly capable in terms of processing power. Although they are already used in computer vision, no comparable measurement experiments of the popular feature extraction algorithm have been made yet. That is, local feature descriptors are widely used in many computer vision applications, and recently various methods have been proposed. While there are many evaluations have focused on various aspects of local features, matching accuracy, however there are no comparisons considering on speed trade-offs of recent descriptors such as ORB, FAST and BRISK. In this paper, we try to provide a performance evaluation of feature descriptors, and compare their matching precision and speed in KD-Tree setup with efficient computation of Hamming distance. The experimental results show that the recently proposed real valued descriptors such as ORB and FAST outperform state-of-the-art descriptors such SIFT and SURF in both, speed-up efficiency and precision/recall.
Keywords
Performance Evaluation; Feature Extraction Algorithm; Mobile Application;
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