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» Dynamic Shape Learning and Forgetting
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ICCS
2005
Springer
14 years 1 months ago
Dynamic Data Driven Coupling of Continuous and Discrete Methods for 3D Tracking
We present a new framework for robust 3D tracking, using a dynamic data driven coupling of continuous and discrete methods to overcome their limitations. Our method uses primarily ...
Dimitris N. Metaxas, Gabriel Tsechpenakis
FGCN
2008
IEEE
175views Communications» more  FGCN 2008»
14 years 2 months ago
Environment Recognition Based on Human Actions Using Probability Networks
To realize context aware applications for smart home environments, it is necessary to recognize function or usage of objects as well as categories of them. On conventional researc...
Hiroshi Miki, Atsuhiro Kojima, Koichi Kise
CLEAR
2006
Springer
145views Biometrics» more  CLEAR 2006»
13 years 11 months ago
A Joint System for Single-Person 2D-Face and 3D-Head Tracking in CHIL Seminars
We present the IBM systems submitted and evaluated within the CLEAR'06 evaluation campaign for the tasks of single person visual 3D tracking (localization) and 2D face trackin...
Gerasimos Potamianos, ZhenQiu Zhang
PAMI
2008
202views more  PAMI 2008»
13 years 7 months ago
Metamorphs: Deformable Shape and Appearance Models
This paper presents a new deformable modeling strategy aimed at integrating shape and appearance in a unified space. If we think traditional deformable models as "active cont...
Xiaolei Huang, Dimitris N. Metaxas
ICCV
2007
IEEE
14 years 9 months ago
The Best of Both Worlds: Combining 3D Deformable Models with Active Shape Models
Reliable 3D tracking is still a difficult task. Most parametrized 3D deformable models rely on the accurate extraction of image features for updating their parameters, and are pro...
Christian Vogler, Zhiguo Li, Atul Kanaujia, Siome ...