An analysis of SIFT object recognition with an emphasis on landmark detection
Author(s)Ross, Benjamin Charles
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Trevor J. Darrell.
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In this thesis, I explore the realm of feature-based object. recognition applied to landmark detection. I have built a system using SIFT object recognition and Locality-Sensitive Hashing to quickly and accurately detect landmarks with accuracies ranging from 85-95%. I have also compared PCA-SIFT, a newly developed feature descriptor, to SIFT, and have found that SIFT outperforms it only particular data set. In addition, I have, performed a relatively extensive empirical comparison between Locality-Sensitive Hashing and Best-Bin First, two approximate nearest neighbor searches, finding that Locality-Sensitive Hashing in general performs the best.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 109-110).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.