Augmented Reality

Mobile phones are central to modern communication, yet for individuals with tremors, the precision required for touch-based interfaces is a significant hurdle. In pursuit of social equality and to empower those with tremors to interact more effectively with mobile technology, this study introduces an optical see-through augmented reality (AR) system equipped with a stabilized filter.

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Mobile phones are central to modern communication, yet for individuals with tremors, the precision required for touch-based interfaces is a significant hurdle. In pursuit of social equality and to empower those with tremors to interact more effectively with mobile technology, this study introduces an optical see-through augmented reality (AR) system equipped with a stabilized filter.

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39 Views

The inquiry into the impact of diverse transitions between cross-reality environments on the user experience remains a compelling research endeavor. 

Existing work often offers fragmented perspectives on various techniques or confines itself to a singular segment of the reality-virtuality spectrum, be it virtual reality or augmented reality.

This study embarks on bridging this knowledge gap by systematically assessing the effects of six prevalent transitions while users remain immersed in tasks spanning both virtual and physical domains. 

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Point cloud streaming has recently attracted research attention as it has the potential to provide six degrees of freedom movement, which is essential for truly immersive media. The transmission of point clouds requires high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Point clouds have been evaluated in Virtual Reality (VR), where viewers are completely immersed in a virtual environment.

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A promising technique to realize augmented reality on future light-weight glasses is to offload computationally extensive rendering tasks to the cloud. This however places considerable demands on the network as well as the air interface with respect to latency, reliability and throughput. For evaluation of these architectures and for traffic modelling, a dataset is provided, which contains realistic payloads of cloud-rendered augmented reality in form of video files.

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Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and  a platform for the visualization of the results as 3D models.

This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.

This dataset relates to 718 of the 1012 LIDC-IDRI scans.

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698 Views