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» Monte Carlo Localization Using SIFT Features
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CGA
2005
14 years 9 months ago
A Novel Monte Carlo Noise Reduction Operator
A novel Monte Carlo noise reduction operator is proposed in this paper. We apply and extend the standard bilateral filtering method and build a new local adaptive noise reduction k...
Ruifeng Xu, Sumanta N. Pattanaik
ICML
1999
IEEE
15 years 10 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
CVPR
2004
IEEE
15 years 11 months ago
PCA-SIFT: A More Distinctive Representation for Local Image Descriptors
Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid [14] recently eva...
Yan Ke, Rahul Sukthankar
68
Voted
TITB
2010
151views Education» more  TITB 2010»
14 years 4 months ago
An adaptive Monte Carlo approach to phase-based multimodal image registration
In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates...
Alexander Wong
69
Voted
SBACPAD
2006
IEEE
102views Hardware» more  SBACPAD 2006»
15 years 3 months ago
Ultra-Fast CPU Performance Prediction: Extending the Monte Carlo Approach
Performance evaluation of contemporary processors is becoming increasingly difficult due to the lack of proper frameworks. Traditionally, cycle-accurate simulators have been exte...
Ram Srinivasan, Jeanine Cook, Olaf M. Lubeck