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» Using a Non-prior Training Active Feature Model
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MVA
2007
309views Computer Vision» more  MVA 2007»
15 years 1 months ago
Robust Facial Feature Extraction Using Embedded Hidden Markov Model for Face Recognition under Large Pose Variation
We propose an algorithm for extracting facial features robustly from images for face recognition under large pose variation. Rectangular facial features are retrieved via the by-p...
Ping-Han Lee, Yun-Wen Wang, Jison Hsu, Ming-Hsuan ...
ICML
2000
IEEE
16 years 2 months ago
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
ICIP
2004
IEEE
16 years 3 months ago
A hidden markov model framework for traffic event detection using video features
We present a novel approach for highway traffic event detection. Our algorithm extracts features directly from the compressed video and automatically detects traffic events using ...
Xiaokun Li, Fatih Murat Porikli
CVPR
2006
IEEE
16 years 3 months ago
Training Deformable Models for Localization
We present a new method for training deformable models. Assume that we have training images where part locations have been labeled. Typically, one fits a model by maximizing the l...
Deva Ramanan, Cristian Sminchisescu
CIVR
2003
Springer
107views Image Analysis» more  CIVR 2003»
15 years 7 months ago
Fast Video Retrieval under Sparse Training Data
Feature selection for video retrieval applications is impractical with existing techniques, because of their high time complexity and their failure on the relatively sparse trainin...
Yan Liu, John R. Kender