44 research outputs found
Inattentional Blindness for Redirected Walking Using Dynamic Foveated Rendering
Redirected walking is a Virtual Reality(VR) locomotion technique which
enables users to navigate virtual environments (VEs) that are spatially larger
than the available physical tracked space. In this work we present a novel
technique for redirected walking in VR based on the psychological phenomenon of
inattentional blindness. Based on the user's visual fixation points we divide
the user's view into zones. Spatially-varying rotations are applied according
to the zone's importance and are rendered using foveated rendering. Our
technique is real-time and applicable to small and large physical spaces.
Furthermore, the proposed technique does not require the use of stimulated
saccades but rather takes advantage of naturally occurring saccades and blinks
for a complete refresh of the framebuffer. We performed extensive testing and
present the analysis of the results of three user studies conducted for the
evaluation
Inattentional Blindness for Redirected Walking Using Dynamic Foveated Rendering
Redirected walking is a Virtual Reality(VR) locomotion technique which enables users to navigate virtual environments (VEs) that are spatially larger than the available physical tracked space. In this work we present a novel technique for redirected walking in VR based on the psychological phenomenon of inattentional blindness. Based on the user's visual fixation points we divide the user's view into zones. Spatially-varying rotations are applied according to the zone's importance and are rendered using foveated rendering. Our technique is real-time and applicable to small and large physical spaces. Furthermore, the proposed technique does not require the use of stimulated saccades but rather takes advantage of naturally occurring saccades and blinks for a complete refresh of the framebuffer. We performed extensive testing and present the analysis of the results of three user studies conducted for the evaluation
Efficient Deduplication and Leakage Detection in Large Scale Image Datasets with a focus on the CrowdAI Mapping Challenge Dataset
Recent advancements in deep learning and computer vision have led to
widespread use of deep neural networks to extract building footprints from
remote-sensing imagery. The success of such methods relies on the availability
of large databases of high-resolution remote sensing images with high-quality
annotations. The CrowdAI Mapping Challenge Dataset is one of these datasets
that has been used extensively in recent years to train deep neural networks.
This dataset consists of 280k training images and 60k testing
images, with polygonal building annotations for all images. However, issues
such as low-quality and incorrect annotations, extensive duplication of image
samples, and data leakage significantly reduce the utility of deep neural
networks trained on the dataset. Therefore, it is an imperative pre-condition
to adopt a data validation pipeline that evaluates the quality of the dataset
prior to its use. To this end, we propose a drop-in pipeline that employs
perceptual hashing techniques for efficient de-duplication of the dataset and
identification of instances of data leakage between training and testing
splits. In our experiments, we demonstrate that nearly 250k(90%)
images in the training split were identical. Moreover, our analysis on the
validation split demonstrates that roughly 56k of the 60k images also appear in
the training split, resulting in a data leakage of 93%. The source code used
for the analysis and de-duplication of the CrowdAI Mapping Challenge dataset is
publicly available at https://github.com/yeshwanth95/CrowdAI_Hash_and_search .Comment: 9 pages, 2 figure
ESPiM: Eye-Strain Probation Model, An Eye-Tracking Analysis Measure for Digital Displays
Eye-strain is a common issue among computer users due to the prolonged
periods they spend working in front of digital displays. This can lead to
vision problems, such as irritation and tiredness of the eyes and headaches. We
propose the Eye-Strain Probation Model (ESPiM), a computational model based on
eye-tracking data that measures eye-strain on digital displays based on the
spatial properties of the user interface and display area for a required period
of time. As well as measuring eye-strain, ESPiM can be applied to compare (a)
different user interface designs, (b) different display devices, and (c)
different interaction techniques. Two user studies were conducted to evaluate
the effectiveness of ESPiM. The first was conducted in the form of an in-person
study with an infrared eye-tracking sensor with 32 participants. The second was
conducted in the form of an online study with a video-based eye-tracking
technique via webcams on users' computers with 13 participants. Our analysis
showed significantly different eye-strain patterns based on the video gameplay
frequency of participants. Further, we found distinctive patterns among users
on a regular 9-to-5 routine versus those with more flexible work hours in terms
of (a) error rates and (b) reported eye-strain symptoms
Effectiveness of an Immersive Virtual Environment (CAVE) for Teaching Pedestrian Crossing to Children with PDD-NOS
Children with Autism Spectrum Disorders (ASD) exhibit a range of developmental disabilities, with mild to severe effects in social interaction and communication. Children with PDD-NOS, Autism and co-existing conditions are facing enormous challenges in their lives, dealing with their difficulties in sensory perception, repetitive behaviors and interests. These challenges result in them being less independent or not independent at all. Part of becoming independent involves being able to function in real world settings, settings that are not controlled. Pedestrian crossings fall under this category: as children (and later as adults) they have to learn to cross roads safely. In this paper, we report on a study we carried out with 6 children with PDD-NOS over a period of four (4) days using a VR CAVE virtual environment to teach them how to safely cross at a pedestrian crossing. Results indicated that most children were able to achieve the desired goal of learning the task, which was verified in the end of the 4-day period by having them cross a real pedestrian crossing (albeit with their parent/educator discretely next to them for safety reasons)
Development and integration of digital technologies addressed to raise awareness and access to European underwater cultural heritage. An overview of the H2020 i-MARECULTURE project
The Underwater Cultural Heritage (UCH)
represents a vast historical and scientific resource that, often, is
not accessible to the general public due the environment and depth
where it is located. Digital technologies (Virtual Museums, Virtual
Guides and Virtual Reconstruction of Cultural Heritage) provide
a unique opportunity for digital accessibility to both scholars and
general public, interested in having a better grasp of underwater
sites and maritime archaeology. This paper presents the
architecture and the first results of the Horizon 2020 iMARECULTURE
(Advanced VR, iMmersive Serious Games and
Augmented REality as Tools to Raise Awareness and Access to
European Underwater CULTURal heritage) project that aims to
develop and integrate digital technologies for supporting the wide
public in acquiring knowledge about UCH. A Virtual Reality (VR)
system will be developed to allow users to visit the underwater sites
through the use of Head Mounted Displays (HMDs) or digital
holographic screens. Two serious games will be implemented for
supporting the understanding of the ancient Mediterranean
seafaring and the underwater archaeological excavations. An
Augmented Reality (AR) system based on an underwater tablet
will be developed to serve as virtual guide for divers that visit the
underwater archaeological sites