Immersive Gesture Interfaces for HMD Based Virtual World Navigation

The goal of this project is to develop a computer algorithm that improves the effectiveness of the data visualization by (semi)automatically computing quantitative information and geometric properties as well as rendering from the biomedical imaging data. The input imaging data can be either static (3D) or time-varying (4D). We aims at creating new knowledge of biomedical field and helping the diagnosis/treatment of disease by applying our quantitative visualization techniques to biomedical imaging data.

Picture


This project design immersive gesture interfaces for HMD based navigation of virtual environments and evaluate their usability. Mass-market head mounted displays (HMDs) are currently attracting a wide interest from consumers because they allow immersive virtual reality (VR) experiences at an affordable cost. VR programs executed on a PC often use traditional input devices such as keyboards or mouse. Such devices hinder the full immersive feeling because manipulating a keyboard or mouse does not resemble the actual action in a VR environment. From this motivation, we aims at designing new gesture interface for HMD based virtual world navigation and giving users feelings of immersion.

Quantitative Visualization of 3D/4D biomedical Imaging Data

The goal of this project is to develop a computer algorithm that improves the effectiveness of the data visualization by (semi)automatically computing quantitative information and geometric properties as well as rendering from the biomedical imaging data. The input imaging data can be either static (3D) or time-varying (4D). We aims at creating new knowledge of biomedical field and helping the diagnosis/treatment of disease by applying our quantitative visualization techniques to biomedical imaging data.

Picture


This project overcomes the limitation of conventional visualization algorithms that are focused on 'rendering' 'static' imaging data and develops quantitative visualization algorithms that can work on time-varying three dimensional imaging data as well as static data. The proposed technique can create new knowledge of biomedical field and can be applied to disease diagnosis and treatment. In addition, our method (semi)automatically computes patient-specific properties from the imaging data that are scanned from a human body, it can significantly reduce the time for medical analysis.