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» Variations on U-Shaped Learning
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CVPR
1999
IEEE
16 years 2 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
118
Voted
CVPR
2005
IEEE
16 years 2 months ago
A Statistical Field Model for Pedestrian Detection
This paper presents a new statistical model for detecting and tracking deformable objects such as pedestrians, where large shape variations induced by local shape deformation can ...
Ying Wu, Ting Yu, Gang Hua
CVPR
2008
IEEE
16 years 2 months ago
Least squares congealing for unsupervised alignment of images
In this paper, we present an approach we refer to as "least squares congealing" which provides a solution to the problem of aligning an ensemble of images in an unsuperv...
Mark Cox, Sridha Sridharan, Simon Lucey, Jeffrey F...
98
Voted
CVPR
2008
IEEE
16 years 2 months ago
Sparsity, redundancy and optimal image support towards knowledge-based segmentation
In this paper, we propose a novel approach to model shape variations. It encodes sparsity, exploits geometric redundancy, and accounts for the different degrees of local variation...
Salma Essafi, Georg Langs, Nikos Paragios
103
Voted
ECCV
2008
Springer
16 years 2 months ago
Similarity Features for Facial Event Analysis
Each facial event will give rise to complex facial appearance variation. In this paper, we propose similarity features to describe the facial appearance for video-based facial even...
Peng Yang, Qingshan Liu, Dimitris N. Metaxas