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» Some Solutions to the Missing Feature Problem in Vision
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82
Voted
ICPR
2008
IEEE
15 years 4 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
CVPR
2010
IEEE
15 years 2 months ago
Local Features Are Not Lonely - Laplacian Sparse Coding for Image Classification
Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization pro...
Shenghua Gao, Wai-Hung Tsang, Liang-Tien Chia, Pei...
65
Voted
ICPR
2010
IEEE
14 years 7 months ago
Performance Evaluation of Automatic Feature Discovery Focused within Error Clusters
We report performance evaluation of our automatic feature discovery method on the publicly available Gisette dataset: a set of 29 features discovered by our method ranks 129 among...
Sui-Yu Wang, Henry S. Baird
ICPR
2008
IEEE
15 years 4 months ago
Improvement of feature matching in catadioptric images using gyroscope data
Most of vision-based algorithms for motion and localization estimation requires matching some interest points in a pair of images. After building feature correspondence, it is pos...
Jean Charles Bazin, Inso Kweon, Cédric Demo...
100
Voted
CVPR
2012
IEEE
13 years 2 days ago
Classifying covert photographs
The advances in image acquisition techniques make recording images never easier and brings a great convenience to our daily life. It raises at the same time the issue of privacy p...
Haitao Lang, Haibin Ling