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» Invariances in kernel methods: From samples to objects
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GRAPHITE
2003
ACM
15 years 11 months ago
Smooth surface reconstruction from noisy range data
This paper shows that scattered range data can be smoothed at low cost by fitting a Radial Basis Function (RBF) to the data and convolving with a smoothing kernel (low pass filt...
Jonathan C. Carr, Richard K. Beatson, Bruce C. McC...
ICML
2004
IEEE
16 years 6 months ago
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm
In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the k...
Zhihua Zhang, Dit-Yan Yeung, James T. Kwok
ACIVS
2006
Springer
15 years 9 months ago
Context-Based Scene Recognition Using Bayesian Networks with Scale-Invariant Feature Transform
Scene understanding is an important problem in intelligent robotics. Since visual information is uncertain due to several reasons, we need a novel method that has robustness to the...
Seung-Bin Im, Sung-Bae Cho
ICCV
2011
IEEE
14 years 6 months ago
BRISK: Binary Robust Invariant Scalable Keypoints
Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of numerous Computer Vision applications. Establishe...
Stefan Leutenegger, Margarita Chli, Roland Y. Sieg...
ICPR
2000
IEEE
16 years 7 months ago
The Multimodal Signature Method: An Efficiency and Sensitivity Study
The multimodal neighbourhood signature (MNS) method has given acceptable results both for the colour-based image retrieval and the object recognition task. Local colour content is...
Dimitri Koubaroulis, Jiri Matas, Josef Kittler