Assessment of consumer VR-headsets’ objective and subjective field of view (FoV) and its feasibility for visual field testing

Virtual reality as a research environment has seen a boost in its popularity during the last decades. Not only the usage fields for this technology have broadened, but also a research niche has appeared as the hardware improved and became more affordable. Experiments in vision research are constructed upon the basis of accurately displaying stimuli with a specific position and size. For classical screen setups, viewing distance and pixel position on the screen define the perceived position for subjects in a relatively precise fashion. However, projection fidelity in HMDs strongly depends on eye and face physiological parameters. This study introduces an inexpensive method to measure the perceived field of view and its dependence upon the eye position and the interpupillary distance, using a super wide angle camera. Measurements of multiple consumer VR headsets show that manufacturers’ claims regarding field of view of their HMDs are mostly unrealistic. Additionally, we performed a “Goldmann” perimetry test in VR to obtain subjective results as a validation of the objective camera measurements. Based on this novel data, the applicability of these devices to test humans’ field of view was evaluated.

Trends in AI Research for the Visual Surveillance of Populations

Progress in artificial intelligence has led to growing concern about the capabilities of AI-powered surveillance systems. This data brief uses bibliometric analysis to chart recent trends in visual surveillance research — what share of overall computer vision research it comprises, which countries are leading the way, and how things have varied over time.

Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM

Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness.

The Open Perimetry Initiative: a framework for cross-platform development for the new generation of portable perimeters

The Open Perimetry Initiative is fully open source and consists of the Open Perimetry Interface (OPI) and an accompanying package (visualFields) with analytical tools. The OPI package contains an ever-growing number of drivers for commercially available perimeters, head-mounted devices, and virtual reality headsets. The visualFields package contains tools for the analysis and visualization of visual field data, including methods to compute deviation values and probability maps. The use of the OPI and visualFields is shown through a custom static automated perimetry test for the full visual field (up to 50° nasally and 80° temporally) developed with the OPI driver for the Octopus 900 and using visualFields for statistical analysis. Its potential for the development of cross-platform apps for driving and testing portable devices is demonstrated with an OPI driver for an Android-based headset. With more than 55 citations in clinical and translational science as listed in Scopus, this initiative has contributed significantly to expanding the knowledge base in perimetry and clinical vision research at large, and with clinical translation. The continued support of researchers, clinicians, industry, and public institutions are key in transforming perimetry research from closed to open science. The Open Perimetry Initiative provides framework to achieve this.

Colour Calibration of a Head Mounted Display for Colour Vision Research Using Virtual Reality

Virtual reality (VR) technology offers vision researchers the opportunity to conduct immersive studies in simulated real-world scenes. However, an accurate colour calibration of the VR head mounted display (HMD), both in terms of luminance and chromaticity, is required to precisely control the presented stimuli. Such a calibration presents significant new challenges, for example, due to the large field of view of the HMD, or the software implementation used for scene rendering, which might alter the colour appearance of objects. Here, we propose a framework for calibrating an HMD using an imaging colorimeter, the I29 (Radiant Vision Systems, Redmond, WA, USA). We examine two scenarios, both with and without using a rendering software for visualisation. In addition, we present a colour constancy experiment design for VR through a gaming engine software, Unreal Engine 4. The colours of the objects of study are chosen according to the previously defined calibration. Results show a high-colour constancy performance among participants, in agreement with recent studies performed on real-world scenarios. Our studies show that our methodology allows us to control and measure the colours presented in the HMD, effectively enabling the use of VR technology for colour vision research.

Why did Karlovich & Wallisch (2021) paste the "Gestalt" label on notions the Gestaltists rejected and disproved?

“Gestalt” is a fashionable buzzword in the vision research community. The people invoking it tend to have little to no understanding of the groundbreaking ideas the term represents, and which they badly misrepresent. This is the case in Karlovich & Wallisch (2021), who misuse Gestalt citations to cover the theoretical gap left by a dominant vision research tradition uninterested in and incapable of addressing problems of shape and organization in vision.