By Nicolas Courty, Elise Arnaud (auth.), Francisco J. Perales, Robert B. Fisher (eds.)
This ebook constitutes the refereed complaints of the fifth overseas convention on Articulated movement and Deformable gadgets, AMDO 2008, held in Port d'Andratx, Mallorca, Spain, in July 2008.
The 36 revised complete papers and seven poster papers offered have been conscientiously reviewed and chosen from sixty four submissions. The papers are prepared in topical part on special effects: human modelling and animation, human movement: research, monitoring, 3D reconstruction and popularity, multimodal consumer interplay: VR and ar, speech, biometrics, and complex multimedia platforms: criteria, listed video contents.
Read Online or Download Articulated Motion and Deformable Objects: 5th International Conference, AMDO 2008, Port d’Andratx, Mallorca, Spain, July 9-11, 2008. Proceedings PDF
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Extra resources for Articulated Motion and Deformable Objects: 5th International Conference, AMDO 2008, Port d’Andratx, Mallorca, Spain, July 9-11, 2008. Proceedings
2. Axis points 3 2D-3D Pose Tracking For our body tracking task, we define a body model as show in Fig. 3. The degrees of freedom (DOF) of the model include the free rotation and translation of the body and waist, shoulders, elbows joints. Each joint rotation is represented as ZXY Euler angles. This gives a total of 21 degrees of freedom (DOF) for the model. The shapes of body segments are modeled as 3D ellipsoids. In , Demirdjian et al. used Iterated Closest Points (ICP) method to match body model to 3D shape data created from stereo vision.
One of the most common approaches for dynamic gesture recognition is using HMM (Hidden Markov Model) or its variants . However, a strong assumption that observations are conditionally independent is usually made in such generative models in order to ensure computational tractability. This restriction makes it difficult or impossible to accommodate long-range dependencies among observations or multiple overlapping features of the observations at multiple time steps. Training Joint Angles Space Hierarchical Manifold Space Training Hierarchical CRF Training Sets Class Labels Hierarchical Manifold Data Joint Angles Trajectories Training Hierarchical CRF Testing HGPLVM Hierarchical CRF Model Joint Angles Trajectories Hierarchical Manifold Data Testing Sets Class Labels Recognition (model inference) Fig.
Learning articulated skeletons from motion. cn Abstract. We describe a hierarchical approach for recognizing continuous hand gestures. It consists of hierarchical nonlinear dimensionality reduction based feature extraction and Hierarchical Conditional Random Field (Hierarchical CRF) based motion modeling. Articulated hands can be decomposed into several hand parts and we explore the underlying structures of articulated action spaces for both the hand and hand parts using Hierarchical Gaussian Process Latent Variable Model (HGPLVM).