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» Multiple Object Tracking Using Local PCA
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145
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CVPR
2006
IEEE
16 years 6 months ago
Shape-Based Approach to Robust Image Segmentation using Kernel PCA
Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within...
Samuel Dambreville, Yogesh Rathi, Allen Tannenbaum
FGR
2006
IEEE
169views Biometrics» more  FGR 2006»
15 years 10 months ago
Combining PCA and LFA for Surface Reconstruction from a Sparse Set of Control Points
This paper presents a novel method for 3D surface reconstruction based on a sparse set of 3D control points. For object classes such as human heads, prior information about the cl...
Reinhard Knothe, Sami Romdhani, Thomas Vetter
159
Voted
AVSS
2005
IEEE
15 years 10 months ago
Multiple object tracking using elastic matching
A novel region-based multiple object tracking framework based on Kalman filtering and elastic matching is proposed. The proposed Kalman filtering-elastic matching model is gener...
Xingzhi Luo, Suchendra M. Bhandarkar
ACCV
2007
Springer
15 years 10 months ago
Probability Hypothesis Density Approach for Multi-camera Multi-object Tracking
Object tracking with multiple cameras is more efficient than tracking with one camera. In this paper, we propose a multiple-camera multiple-object tracking system that can track 3D...
Nam Trung Pham, Weimin Huang, S. H. Ong
CVPR
1999
IEEE
16 years 6 months ago
A Multiple Hypothesis Approach to Figure Tracking
This paper describes a probabilistic multiple-hypothesis framework for tracking highly articulated objects. In this framework, the probability density of the tracker state is repr...
Tat-Jen Cham, James M. Rehg