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ICPR
2008
IEEE
15 years 8 months ago
Non-linear feature extraction by linear PCA using local kernel
This paper presents how to extract non-linear features by linear PCA. KPCA is effective but the computational cost is the drawback. To realize both non-linearity and low computati...
Kazuhiro Hotta
138
Voted
CVPR
2010
IEEE
15 years 8 months ago
Putting local features on a Manifold
Local features have proven very useful for recognition. Manifold learning has proven to be a very powerful tool in data analysis. However, manifold learning application for imag...
Marwan Torki and Ahmed Elgammal
CVPR
2004
IEEE
15 years 5 months ago
Face Localization via Hierarchical CONDENSATION with Fisher Boosting Feature Selection
We formulate face localization as a Maximum A Posteriori Probability(MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribu...
Jilin Tu, ZhenQiu Zhang, Zhihong Zeng, Thomas S. H...
142
Voted
ICRA
2000
IEEE
145views Robotics» more  ICRA 2000»
15 years 5 months ago
Feature Based Condensation for Mobile Robot Localization
Much attention has been given to CONDENSATION methods for mobile robot localization. This has resulted in somewhat of a breakthrough in representing uncertainty for mobile robots....
Patric Jensfelt, David J. Austin, Olle Wijk, Magnu...
156
Voted
SISAP
2010
IEEE
259views Data Mining» more  SISAP 2010»
14 years 11 months ago
kNN based image classification relying on local feature similarity
In this paper, we propose a novel image classification approach, derived from the kNN classification strategy, that is particularly suited to be used when classifying images descr...
Giuseppe Amato, Fabrizio Falchi