Articulated Motion and Deformable Objects: 7th International by Albert Clapés, Miguel Reyes, Sergio Escalera (auth.),

By Albert Clapés, Miguel Reyes, Sergio Escalera (auth.), Francisco J. Perales, Robert B. Fisher, Thomas B. Moeslund (eds.)

This ebook constitutes the refereed court cases of the seventh overseas convention on Articulated movement and Deformable items, AMDO 2012, held in Port d'Andratx, Mallorca, Spain, in July 2012. The 27 papers offered have been rigorously reviewed and chosen from forty four submissions. the quantity additionally comprises one complete paper size invited speak. The convention handled the subsequent subject matters: complex special effects (human modeling and animation); human movement (analysis, monitoring, 3D reconstruction and recognition); multimodal consumer interplay and functions; and affective interfaces (recognition and interpretation of feelings, ECAs -- embodied conversational brokers in HCI).

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The pose estimation framework described by Moschini and Fusiello [12] used the hierarchical iterative closest point algorithm to fit a stick figure model to a set of data points on the skeleton curve. The method can recover from errors in matching, but requires manual initialisation. The approach presented by Menier et al. [9] uses Delauney triangulation to extract a set of skeleton points from a closed surface visual hull representation. A generative skeletal model is fitted to the data skeleton points using maximum a posteriori estimation.

Specifically, we use the medioid–shift algorithm [16] with a Gaussian kernel, where the bandwidth is selected to achieve the correct number of clusters. e. 10 walking sequences and 10 running sequences. We combine all pairs of sequences from different classes, which gives us 100 clustering tasks with two clusters. We then measure the percentage of data points assigned to the correct class and report the average. When using the angle metric we assign 60% of the data to the correct class and 65% when using the approximate joint position geodesic distance, which is a small but noticeable improvement.

I-Direct PCA II-PCA differential coord. III-Layered representation IV-Deformer Vi -Deformer+basis Vii -Deform+basis 10% c. 8 Storage cost (MB) Fig. 5. Comparison of the storage cost vs committed error for all compression techniques presented in the paper, applied to the walk sequence. 8MB. Table 1. 21 22 M. Tejera and A. Hilton Table 2. Results. Error is expressed in mm and storage cost in MB. DirectPCA-k = Direct PCA with k basis vectors; LaplPCA-k = PCA using differential coordinates with k basis vectors; LayeredPCA-k = PCA on the difference sequence with k basis vectors; Deformer-c% = Laplacian deformation using c% vertices as constraints for the deformation.

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