Human Motion - Understanding, Modeling, Capture and Animation Second Workshop, HumanMotion 2007, Rio de Janeiro, Brazil, October 20, 2007, Proceedings
This LNCS volume contains the papers presented at the second Workshop on Human Motion Understanding, Modeling, Capture and Animation, which took place on October 20th, 2007, accompanying the 11th IEEE International C- ference on Computer Vision in Rio de Janeiro, Brazil. In total, 38 papers were sub...
Other Authors: | , , |
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Format: | eBook |
Language: | English |
Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2007, 2007
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Edition: | 1st ed. 2007 |
Series: | Image Processing, Computer Vision, Pattern Recognition, and Graphics
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Subjects: | |
Online Access: | |
Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Motion Capture and Pose Estimation
- Marker-Less 3D Feature Tracking for Mesh-Based Human Motion Capture
- Boosted Multiple Deformable Trees for Parsing Human Poses
- Gradient-Enhanced Particle Filter for Vision-Based Motion Capture
- Multi-activity Tracking in LLE Body Pose Space
- Exploiting Spatio-temporal Constraints for Robust 2D Pose Tracking
- Efficient Upper Body Pose Estimation from a Single Image or a Sequence
- Real-Time and Markerless 3D Human Motion Capture Using Multiple Views
- Modeling Human Locomotion with Topologically Constrained Latent Variable Models
- Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing
- Body and Limb Tracking and Segmentation
- 3D Hand Tracking in a Stochastic Approximation Setting
- Nonparametric Density Estimation with Adaptive, Anisotropic Kernels for Human Motion Tracking
- Multi Person Tracking Within Crowded Scenes
- Joint Appearance and Deformable Shape for Nonparametric Segmentation
- Robust Spectral 3D-Bodypart Segmentation Along Time
- Articulated Object Registration Using Simulated Physical Force/Moment for 3D Human Motion Tracking
- An Ease-of-Use Stereo-Based Particle Filter for Tracking Under Occlusion
- Activity Recognition
- Semi-Latent Dirichlet Allocation: A Hierarchical Model for Human Action Recognition
- Recognizing Activities with Multiple Cues
- Human Action Recognition Using Distribution of Oriented Rectangular Patches
- Human Motion Recognition Using Isomap and Dynamic Time Warping
- Behavior Histograms for Action Recognition and Human Detection
- Learning Actions Using Robust String Kernels