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» Approximate Learning of Dynamic Models
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CVPR
2011
IEEE
14 years 9 months ago
Intrinsic Dense 3D Surface Tracking
This paper presents a novel intrinsic 3D surface distance and its use in a complete probabilistic tracking framework for dynamic 3D data. Registering two frames of a deforming 3D ...
Yun Zeng, Chaohui Wang, Yang Wang, David Gu, Dimit...
167
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ECCV
2004
Springer
16 years 3 months ago
A Boosted Particle Filter: Multitarget Detection and Tracking
The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Ga...
Kenji Okuma, Ali Taleghani, Nando de Freitas, Jame...
CVPR
2010
IEEE
15 years 10 months ago
Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes
A novel method for crowd flow modeling and anomaly detection is proposed for both coherent and incoherent scenes. The novelty is revealed in three aspects. First, it is a unique ut...
Shandong Wu, Brian E. Moore, and Mubarak Shah
MIR
2010
ACM
217views Multimedia» more  MIR 2010»
15 years 7 months ago
Feature selection for content-based, time-varying musical emotion regression
In developing automated systems to recognize the emotional content of music, we are faced with a problem spanning two disparate domains: the space of human emotions and the acoust...
Erik M. Schmidt, Douglas Turnbull, Youngmoo E. Kim
ECCV
2004
Springer
16 years 3 months ago
A Bayesian Framework for Multi-cue 3D Object Tracking
This paper presents a Bayesian framework for multi-cue 3D object tracking of deformable objects. The proposed spatio-temporal object representation involves a set of distinct linea...
Jan Giebel, Dariu Gavrila, Christoph Schnörr