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Research Highlight Top Story
Development of VR Sickness Assessment Deep Network Considering Exceptional Motion for VR Video
Viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness can occur when watching immersive VR content. To deal with viewing safety for VR content, objective assessment of VR sickness is of great importance. In this work, based on a deep generative model, we propose a novel objective VR sickness assessment (VRSA) network to automatically predict VR sickness. The proposed method takes into account motion patterns of VR videos in which exceptional motion is a critical factor inducing excessive VR sickness in human motion perception. The proposed VRSA network consists of two parts, the VR video generator and VR sickness score predictor. For the evaluation of VRSA performance, we performed comprehensive experiments with 360° videos (stimuli), corresponding physiological signals, and subjective questionnaires. We demonstrated that the proposed VRSA achieved a high correlation with human perceptual score for VR sickness....read more
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Research Highlight
Explainable AI can predict prognosis-specific genotype of brain tumor without invasive biopsy
Professor Bumseok Jeong has developed an explainable artificial intelligence model with high diagnostic performance that predicts the IDH genotype of gliomas; this is crucial in treatment planning and prognosis prediction....read more
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Research Highlight
3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents
Professor Jong-Hwan Kim’s research team defined a 3-D scene graph, which represents physical environments in a sparse and semantic way....read more
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Research Highlight
Decision-Level Fusion Method for Emotion Recognition using Multimodal Emotion Recognition Information
We confirmed which combination of features of multi-modal emotion recognition achieves the highest accuracy....read more
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Research Highlight
Research and Development of AI-powered Autonomous Cars in Seoul for Smart Cities
Professor Hyunchul Shim’s automotive driving research team participated in a technology demonstration organized by the Ministry of Land, Infrastructure and Transport, Korea. The event was held on Yeongdong boulevard adjacent to COEX, Seoul on June 17th. ...read more
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Research Highlight
Deep learning for accelerated ultrasound imaging
To accelerate US imaging systems, Prof Jong Chul Ye’s team designed a deep learning technique that improved acquisition speed without compromising the image quality....read more
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Research Highlight
Rapid Detection of Anthrax Spores using Artificial Intelligence and Holographic Microscopy
Combining holography with deep learning enables rapid optical screening of anthrax spores as well as other pathogens....read more
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