By Francisco José Perales, José Santos-Victor
This publication constitutes the refereed lawsuits of the eighth overseas convention on Articulated movement and Deformable items, AMDO 2014, held in Palma de Mallorca, Spain, in July 2014. The 18 papers offered have been rigorously reviewed and chosen from 37 submissions. The convention handled the subsequent themes: geometric and actual deformable types; movement research; articulated versions and animation; modeling and visualization of deformable versions; deformable version purposes; movement research functions; unmarried or a number of human movement research and synthesis; face modeling, monitoring, getting better and popularity versions; digital and augmented truth; haptics units; biometric techniques.
Read or Download Articulated Motion and Deformable Objects: 8th International Conference, AMDO 2014, Palma de Mallorca, Spain, July 16-18, 2014. Proceedings PDF
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Additional resources for Articulated Motion and Deformable Objects: 8th International Conference, AMDO 2014, Palma de Mallorca, Spain, July 16-18, 2014. Proceedings
We perform better for most of the body parts but we have achieved worse results for the lower arms. This happens because the lower arms are often fully occluded and then the forest predicts an average pose. 28 5 V. Belagiannis et al. Conclusion We have presented a holistic model for human pose estimation from 2D images. The model has been built on Random Forests and image patches. We have demonstrated that our formulation delivers state-of-the-art results by evaluating on two datasets and comparing with other approaches.
1. Two proposed conﬁgurations of the self-contained mobile device. Left and right images show coaxialized and non-coaxialized designs respectively. 2 General Computing Paradigm Fig. 1. In this diagram, one arrow represents two piece of information regarding visual RGB and infrared wavelengths. Inputs to the system are one RGB image and one infrared image captured by the multi-band camera. Outputs from the system are two projected images—one visible RGB image for foreground interaction with users and one invisible infrared image for facilitating background analysis or M2M communication.
Then, a tree is built from a set of nodes which include binary split functions. Each node encloses a split function θ which is deﬁned on the values of the HOG features of the patch. The HOG feature vector of the image patch is extracted as in . The binary split function determines if a p sample image patch will go to the left Pl or right Pr subset of samples. In particular, the split function is a threshold on one dimension of the HOG feature vector. Among the dimensions of the HOG feature vector, the threshold that gained the best split deﬁnes the split function: (1) θ∗ = arg max g(θ) θ where g(θ) corresponds to the information gain.