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Edward Rosten
Researcher at University of Cambridge
Publications - 49
Citations - 11374
Edward Rosten is an academic researcher from University of Cambridge. The author has contributed to research in topics: Feature extraction & Deep learning. The author has an hindex of 17, co-authored 48 publications receiving 10500 citations. Previous affiliations of Edward Rosten include Los Alamos National Laboratory.
Papers
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Book ChapterDOI
Machine learning for high-speed corner detection
Edward Rosten,Tom Drummond +1 more
TL;DR: It is shown that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time.
Journal Article
Machine Learning for High-Speed Corner Detection
Edward Rosten,Tom Drummond +1 more
TL;DR: In this paper, the same scene viewed from two different positions should yield features which correspond to the same real-world 3D locations, and a comparison of corner detectors based on this criterion applied to 3D scenes is made.
Journal ArticleDOI
Faster and Better: A Machine Learning Approach to Corner Detection
TL;DR: A new heuristic for feature detection is presented and, using machine learning, a feature detector is derived from this which can fully process live PAL video using less than 5 percent of the available processing time.
Proceedings ArticleDOI
Fusing points and lines for high performance tracking
Edward Rosten,Tom Drummond +1 more
TL;DR: This paper presents a method for integrating the two systems and robustly combining the pose estimates they produce, and shows how on-line learning can be used to improve the performance of feature tracking.
Journal ArticleDOI
Bayesian localization microscopy reveals nanoscale podosome dynamics
Susan Cox,Edward Rosten,James Monypenny,Tijana Jovanovic-Talisman,Dylan T. Burnette,Jennifer Lippincott-Schwartz,Gareth E. Jones,Rainer Heintzmann,Rainer Heintzmann +8 more
TL;DR: A localization microscopy analysis method that is able to extract results in live cells using standard fluorescent proteins and xenon arc lamp illumination and was able to reveal the nanoscale dynamics of podosome formation and dissociation throughout an entire cell with a resolution of 50 nm on a 4-s timescale.