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By Francisco José Perales, José Santos-Victor

This publication constitutes the refereed court cases of the eighth overseas convention on Articulated movement and Deformable gadgets, AMDO 2014, held in Palma de Mallorca, Spain, in July 2014. The 18 papers provided have been conscientiously reviewed and chosen from 37 submissions. The convention handled the subsequent issues: geometric and actual deformable versions; movement research; articulated types and animation; modeling and visualization of deformable types; deformable version functions; movement research purposes; unmarried or a number of human movement research and synthesis; face modeling, monitoring, improving and popularity types; digital and augmented fact; haptics units; biometric techniques.

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Extra resources for Articulated Motion and Deformable Objects: 8th International Conference, AMDO 2014, Palma de Mallorca, Spain, July 16-18, 2014. Proceedings

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The information gain can be formulated as: g(θ) = H(P ) − i∈{l,r} |Pi (θ)| H(Pi (θ)) |P | (2) where H(P ) is the entropy. For estimating the entropy, the sum-of-squaresdifferences is used: 2 H(P ) = vp,j − μj 2 (3) p∈P j where the vector vp,j includes the offsets for each joint j from the image patch centre and μj denotes the mean for each joint offset. In order to estimate the mean μj , we introduce a threshold ρ to consider only joints that are close to the sampled patch, similar to [14]. Finally, the tree grows until it reaches the maximum depth, the minimum number of samples per leaf or the information gain for the node drops below a threshold.

In: IEEE CVPR, pp. 1–8 (2008) 12. : Sparse probabilistic regression for activity-independent human pose inference. In: IEEE CVPR, pp. 1–8 (2008) 13. : Random forests. Machine Learning 45(1), 5–32 (2001) 14. : Efficient regression of general-activity human poses from depth images. In: IEEE ICCV, pp. 415–422 (2011) 15. : Real-time human pose recognition in parts from single depth images. Communications of the ACM 56(1), 116–124 (2013) 16. : Discriminative appearance models for pictorial structures.

We demonstrate the power of our method in two publicly available datasets and propose a third one. Finally, we achieve state-of-the-art results in comparison to other approaches. 1 Introduction Human pose estimation from single images is a fundamental problem in Computer Vision [1]. It has a wide range of potential applications such as surveillance, health care and human computer interaction. Real life applications involve a huge amount of human appearance variations. Furthermore, out of studio environments usually include dynamic background and clutter.

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