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» Unsupervised Learning of Invariant Features Using Video
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CVPR
2008
IEEE
14 years 9 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
CVPR
2009
IEEE
15 years 2 months ago
Learning Invariant Features Through Topographic Filter Maps
Several recently-proposed architectures for highperformance object recognition are composed of two main stages: a feature extraction stage that extracts locallyinvariant feature...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergu...
ACIVS
2006
Springer
14 years 1 months ago
Motion Blur Concealment of Digital Video Using Invariant Features
This paper deals with concealment of motion blur in image sequences. The approach is different from traditional methods, which attempt to deblur the image. Our approach utilizes t...
Ville Ojansivu, Janne Heikkilä
ICIP
2003
IEEE
14 years 9 months ago
Feature selection for unsupervised discovery of statistical temporal structures in video
We present algorithms for automatic feature selection for unsupervised structure discovery from video sequences. Feature selection in this scenario is hard because of the absence ...
Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang...
AVSS
2009
IEEE
13 years 8 months ago
Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using Unsupervised Learning
This paper presents a novel approach to vehicle detection in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to ...
Birgi Tamersoy, Jake K. Aggarwal