Konversatorium on Friday, June 8, 2018 - 10:30
Jiří Hladůvka received his MSc in mathematics from Comenius University in Bratislava in 1996 and PhD in computer science from Vienna University of Technology in 2002. From 2003 till 2018 he was a senior researcher at VRVis. His research interests include processing and visualization of 2D/3D/nD medical image data, computer vision, pattern recognition, machine learning and deep learning methods for biomedical applications. He served as a reviewer for major conferences and journals in the field of visual computing, including Medical Image Analysis, Transactions on Medical Imaging, MICCAI, IEEE Visualization, and Eurographics. He was a coeditor of the Central European Seminar on Computer Graphics.
I have 1 paper published in IEEE International Workshop on Multimedia Signal Processing (MMSP) - 2017. My research work mainly comprises of graph signal processing and graph topology estimation in which most of the computations were done using algebraic optimizations. It has various applications from which I have worked in image and video denoising. Apart from graph signal processing, Apart from graph signals, I have also worked in image processing, video processing and speech signals.
Gautier Maufoy received his scientific Baccalauréat ( high school diploma in France ) in 2014. From 2014 till 2016 Gautier Maufoy studied math and physics in a preparatory classes for french engineering school in Metz. From 2016 till 2018 he studied computer science in the ENSICAEN, a french engineering school in Caen.
Evaluation of the Recognition Distances of Safety Signs in VR Considering Vision Impairments (Epilog-Testtalk)
To facilitate the safe evacuation of buildings, escape-route safety signs need to be placed along the whole escape route such that they are legible for building occupants. While standards and legal requirements provide suggestions on how to select and place safety signs to achieve this, they do not provide sufficient considerations concerning people suffering from vision impairments. A main cause of vision impairment are age-related eye diseases, with the most common symptom being the loss of visual acuity.
We investigate the influence of visual acuity on the ability to recognize safety signs using a novel methodology, evaluating existing standards concerning vision impairments: We calibrate the visual acuity of the test subjects to the same level via a standardized medical test in VR. This is achieved by using test subjects with normal or corrected vision and simulating the impairment in VR. Furthermore, we present a tool for lighting designers which enables them to check their designs considering maximum recognition distances to investigate problematic areas along an escape route.
Using our novel user-study methodology, we determined the recognition distances for safety signs, observed under two different levels of visual acuity and varying observation angles. In addition, we determined the impact of the HTC Vive’s HMD on the visual acuity achievable in VR. We conclude that the existing standards fail to correctly estimate the maximum recognition distances of safety signs for observers suffering from reduced visual acuity.