Multi-Attributed Graph Matching With Multi-Layer Graph Structure and Multi-Layer Random Walks.

This paper addresses the multi-attributed graph matching problem, which considers multiple attributes jointly while preserving the characteristics of each attribute for graph matching. Since most of conventional graph matching algorithms integrate multiple attributes to construct a single unified attribute in an oversimplified manner, the information from multiple attributes is often not completely utilized. In order to solve this problem, we propose a novel multi-layer graph structure that can preserve the characteristics of each attribute in separated layers, and also propose a multi-attributed graph matching algorithm based on random walk centrality with the proposed multi-layer graph structure. We compare the proposed algorithm with other state-of-the-art graph matching algorithms based on a single-layer structure using synthetic and real data sets and demonstrate the superior performance of the proposed multi-layer graph structure and the multi-attributed graph matching algorithm.

A wellness platform for stereoscopic 3D video systems using EEG-based visual discomfort evaluation technology.

Recent advances in three-dimensional (3D) video technology have extended the range of our experience while providing various 3D applications to our everyday life. Nevertheless, the so-called visual discomfort (VD) problem inevitably degrades the quality of experience in stereoscopic 3D (S3D) displays. Meanwhile, electroencephalography (EEG) has been regarded as one of the most promising brain imaging modalities in the field of cognitive neuroscience. In an effort to facilitate comfort with S3D displays, we propose a new wellness platform using EEG. We first reveal features in EEG signals that are applicable to practical S3D video systems as an index for VD perception. We then develop a framework that can automatically determine severe perception of VD based on the EEG features during S3D video viewing by capitalizing on machine-learning-based braincomputer interface technology. The proposed platform can cooperate with advanced S3D video systems whose stereo baseline is adjustable. Thus, the optimal S3D content can be reconstructed according to a viewer's sensation of VD. Applications of the proposed platform to various S3D industries are suggested, and further technical challenges are discussed for follow-up research.