Towards Fully Mobile 3D Face, Body, and Environment Capture Using Only Head-worn Cameras.

We propose a new approach for 3D reconstruction of dynamic indoor and outdoor scenes in everyday environments, leveraging only cameras worn by a user. This approach allows 3D reconstruction of experiences at any location and virtual tours from anywhere. The key innovation of the proposed ego-centric reconstruction system is to capture the wearer's body pose and facial expression from near-body views, e.

Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification.

In order to establish the correspondences between different brains for comparison, spatial normalization based morphometric measurements have been widely used in the analysis of Alzheimer's disease (AD). In the literature, different subjects are often compared in one atlas space, which may be insufficient in revealing complex brain changes. In this paper, instead of deploying one atlas for feature extraction and classification, we propose a maximum-margin based representation learning (MMRL) method to learn the optimal representation from multiple atlases.

Multiple-network classification of childhood autism using functional connectivity dynamics.

Characterization of disease using stationary resting-state functional connectivity (FC) has provided important hallmarks of abnormal brain activation in many domains. Recent studies of resting-state functional magnetic resonance imaging (fMRI), however, suggest there is a considerable amount of additional knowledge to be gained by investigating the variability in FC over the course of a scan. While a few studies have begun to explore the properties of dynamic FC for characterizing disease, the analysis of dynamic FC over multiple networks at multiple time scales has yet to be fully examined.

Disrupted brain functional network in internet addiction disorder: a resting-state functional magnetic resonance imaging study.

Internet addiction disorder (IAD) is increasingly recognized as a mental health disorder, particularly among adolescents. The pathogenesis associated with IAD, however, remains unclear. In this study, we aim to explore the encephalic functional characteristics of IAD adolescents at rest using functional magnetic resonance imaging data.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.

Survey: Enhancing protein complex prediction in PPI networks with GO similarity weighting.

Predicting protein complexes from protein-protein interaction (PPI) networks has been the focus of many computational approaches over the last decade. These methods tend to vary in performance based on the structure of the network and the parameters provided to the algorithm. Here, we evaluate the merits of enhancing PPI networks with semantic similarity edge weights using Gene Ontology (GO) and its annotation data.