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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

X. Luo, M. Feuerstein, D. Deguchi, T. Kitasaka, Y. Suenaga, K. Imaizumi, Y. Hasegawa, K. Mori
A Study on Feature Point Extraction from Bronchoscopic Images for Bronchoscope Tracking
17th Meeting of the Japan Society of Computer Assisted Surgery, Tokyo, Japan, October/November 2008 (bib)

Recently, feature-based tracking has received major attention in the research community. It requires a continuous extraction of stable features in the camera images. This paper presents NMF-SIFT, an extension to the well-known SIFT (Scale Invariant Feature Transform) algorithm based on NMF (Non-negative Matrix Factorization). A preliminary study, which compares NMF-SIFT to three standard algorithms for feature extraction (Harris, Kanade-Lucas-Tomasi, and SIFT) in bronchoscopic images, shows that the complexity of the SIFT keypoint descriptor can be reduced by NMF-SIFT while keeping a high number of keypoints for each image.
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