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» MuFeSaC: Learning When to Use Which Feature Detector
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ICIP
2007
IEEE
14 years 6 months ago
MuFeSaC: Learning When to Use Which Feature Detector
Interest point detectors are the starting point in image analysis for depth estimation using epipolar geometry and camera ego-motion estimation. With several detectors defined in ...
Sreenivas R. Sukumar, David L. Page, Hamparsum Boz...
UMUAI
2008
108views more  UMUAI 2008»
13 years 4 months ago
Developing a generalizable detector of when students game the system
Some students, when working in interactive learning environments, attempt to "game the system", attempting to succeed in the environment by exploiting properties of the s...
Ryan Shaun Joazeiro de Baker, Albert T. Corbett, I...
BMCBI
2007
153views more  BMCBI 2007»
13 years 4 months ago
Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis
Background: A nanopore detector has a nanometer-scale trans-membrane channel across which a potential difference is established, resulting in an ionic current through the channel ...
Matthew Landry, Stephen Winters-Hilt
GECCO
2008
Springer
206views Optimization» more  GECCO 2008»
13 years 6 months ago
Improving accuracy of immune-inspired malware detectors by using intelligent features
In this paper, we show that a Bio-inspired classifier’s accuracy can be dramatically improved if it operates on intelligent features. We propose a novel set of intelligent feat...
M. Zubair Shafiq, Syed Ali Khayam, Muddassar Faroo...
CORR
2008
Springer
193views Education» more  CORR 2008»
13 years 4 months ago
Faster and better: a machine learning approach to corner detection
The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is importand because the same scene vi...
Edward Rosten, Reid Porter, Tom Drummond