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Tag: Deep learning
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    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

    360-degree Video AI Applications Deep learning Human Perception Image and Video sYstems (IVY) Laboratory KI for Artificial Intelligence Motion Mismatch Objective Assessment Virtual Reality (VR) VR Sickness
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    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

    Brain Imaging and Neuromodulation Computational Neuroscience Deep learning KI for Health Science and Technology Laboratory of Computational Affective Neuroscience and Development Neuroimaging and Neuromodulation
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    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

    3-D Scene Graph AI AI Applications ASIT RITL (Robot Intelligent Technology Lab) Deep learning Environment Model Human Robot Interaction Intelligent Agent KI for Artificial Intelligence Robot Intelligence Scene Graph Scene Understanding
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    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

    AI Applications Deep learning Facial Expression Recognition HRI Human Robot Interaction KAIST Institute for Artificial Intelligence
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    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

    Autonomous driving Deep learning USRG(Unmanned System Research Group)
  • 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

    B-mode Deep learning Hankel matrix multi-line acquisition Ultrasound imaging
  • 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

    ADD Deep learning Holography KIHST Optics
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