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» New Features to Identify Computer Generated Images
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ECCV
2008
Springer
15 years 6 months ago
Unsupervised Classification and Part Localization by Consistency Amplification
We present a novel method for unsupervised classification, including the discovery of a new category and precise object and part localization. Given a set of unlabelled images, som...
Leonid Karlinsky, Michael Dinerstein, Dan Levi, Sh...
157
Voted
CIKM
2009
Springer
15 years 8 months ago
Efficient feature weighting methods for ranking
Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generali...
Hwanjo Yu, Jinoh Oh, Wook-Shin Han
CVPR
2008
IEEE
16 years 6 months ago
Boosting ordinal features for accurate and fast iris recognition
In this paper, we present a novel iris recognition method based on learned ordinal features.Firstly, taking full advantages of the properties of iris textures, a new iris represen...
Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu,...
IJCV
2008
106views more  IJCV 2008»
15 years 4 months ago
A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences
Given an image sequence of a scene consisting of multiple rigidly moving objects, multi-body structure-and-motion (MSaM) is the task to segment the image feature tracks into the d...
Konrad Schindler, David Suter, Hanzi Wang
ICIAR
2009
Springer
15 years 9 months ago
Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models
A self-adaptive Hidden Markov Model (SA-HMM) based framework is proposed for behavior recognition in this paper. In this model, if an unknown sequence cannot be classified into an...
Jun Yin, Yan Meng