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» Monte Carlo Localization Using SIFT Features
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AUSAI
2006
Springer
15 years 1 months ago
Learning Hybrid Bayesian Networks by MML
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditi...
Rodney T. O'Donnell, Lloyd Allison, Kevin B. Korb
CRV
2007
IEEE
137views Robotics» more  CRV 2007»
15 years 4 months ago
Quantitative Evaluation of Feature Extractors for Visual SLAM
We present a performance evaluation framework for visual feature extraction and matching in the visual simultaneous localization and mapping (SLAM) context. Although feature extra...
Jonathan Klippenstein, Hong Zhang
ICCV
2007
IEEE
15 years 11 months ago
Perspectively Invariant Normal Features
We extend the successful 2D robust feature concept into the third dimension in that we produce a descriptor for a reconstructed 3D surface region. The descriptor is perspectively ...
Kevin Köser, Reinhard Koch
ICML
2005
IEEE
15 years 10 months ago
Tempering for Bayesian C&RT
This paper concerns the experimental assessment of tempering as a technique for improving Bayesian inference for C&RT models. Full Bayesian inference requires the computation ...
Nicos Angelopoulos, James Cussens
ICPR
2008
IEEE
15 years 10 months ago
Unsupervised image embedding using nonparametric statistics
Embedding images into a low dimensional space has a wide range of applications: visualization, clustering, and pre-processing for supervised learning. Traditional dimension reduct...
Guobiao Mei, Christian R. Shelton