Vers une conception accessible de la lecture d'actualités en réalité virtuelle pour la basse vision

Low-vision conditions resulting in partial loss of the visual field strongly affect patients' daily tasks and routines, and none more prominently than the ability to access text. Though vision aids such as magnifiers, digital screens, and text-to-speech devices can improve overall accessibility to text, news media, which is non-linear and has complex and volatile formatting, is still inaccessible, barring low-vision patients from easy access to essential news content. This paper positions virtual reality as the next step towards accessible and enjoyable news reading for the low vision. We first conduct an extensive review into existing research on low-vision reading technologies and accessibility for modern news media. From previous research and studies, we then conduct an analysis into the advantages of virtual reality for low-vision reading and propose comprehensive guidelines for visual accessibility design in virtual reality, with a focus on reading. This is coupled with a hands-on study of eight reading applications in virtual reality to evaluate how accessibility design is currently implemented in existing products. Finally, we present a framework that integrates the design principles resulting from our analysis and study, and implement a proof-of-concept for this framework using browser-based graphics to demonstrate the feasibility of our proposal with modern virtual reality technology.Les conditions de basse vision entraînant une perte partielle du champ visuel affectent fortement les tâches et les routines quotidiennes des patients, et aucune de celles-ci n'est plus notable que la possibilité d'accéder à du texte. Bien que les aides visuelles telles que les loupes, les écrans numériques et les dispositifs de synthèse vocale puissent améliorer l’accessibilité globale au texte, les médias d’information, qui sont non linéaires et présentent un formatage complexe et volatile, sont toujours inaccessibles, empêchant ainsi les patients malvoyants d’avoir accès aux contenus des nouvelles.Ce papier positionne la réalité virtuelle comme la prochaine étape vers une lecture de nouvelles pour la basse vision, qui soit accessible et agréable. Nous menons d’abord une étude approfondie des recherches existantes sur les technologies de lecture en basse vision et l’accessibilité des médias d’information modernes. À partir de recherches et d’études antérieures, nous analysons ensuite les avantages de la réalité virtuelle pour la lecture en basse vision et proposons des directives globales pour la conception de l’accessibilité visuelle en réalité virtuelle, en mettant l’accent sur la lecture. Ceci est associé à une étude pratique de huit applications de lecture en réalité virtuelle pour évaluer comment cette conception de l'accessibilité est actuellement mise en œuvre dans les produits existants. Enfin, nous présentons un cadre qui intègre les principes de conception résultant de nos analyses et études et nous implémentons une preuve de concept en utilisant des graphiques basés sur un navigateur afin de démontrer la faisabilité de notre proposition avec la technologie moderne de réalité virtuelle

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question

Saliency prediction in 360° architectural scenes:Performance and impact of daylight variations

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question.</p

Saliency prediction in 360° architectural scenes:Performance and impact of daylight variations

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question.</p

Toolbox and dataset for the development of saliency and scanpath models for omnidirectional/360° still images

International audienc

Investigating human-perceptual properties of "shapes" using 3D shapes and 2D fonts

Shapes are generally used to convey meaning. They are used in video games, films and other multimedia, in diverse ways. 3D shapes may be destined for virtual scenes or represent objects to be constructed in the real-world. Fonts add character to an otherwise plain block of text, allowing the writer to make important points more visually prominent or distinct from other text. They can indicate the structure of a document, at a glance. Rather than studying shapes through traditional geometric shape descriptors, we provide alternative methods to describe and analyse shapes, from a lens of human perception. This is done via the concepts of Schelling Points and Image Specificity. Schelling Points are choices people make when they aim to match with what they expect others to choose but cannot communicate with others to determine an answer. We study whole mesh selections in this setting, where Schelling Meshes are the most frequently selected shapes. The key idea behind image Specificity is that different images evoke different descriptions; but ‘Specific’ images yield more consistent descriptions than others. We apply Specificity to 2D fonts. We show that each concept can be learned and predict them for fonts and 3D shapes, respectively, using a depth image-based convolutional neural network. Results are shown for a range of fonts and 3D shapes and we demonstrate that font Specificity and the Schelling meshes concept are useful for visualisation, clustering, and search applications. Overall, we find that each concept represents similarities between their respective type of shape, even when there are discontinuities between the shape geometries themselves. The ‘context’ of these similarities is in some kind of abstract or subjective meaning which is consistent among different people