The advancement of image restoration, especially in reconstructing missing or damaged image areas, has benefited significantly from self-supervised learning techniques, notably through the recent Masked Auto Encoder (MAE) strategy. In this project, we leverage eye-tracking data to enhance image reconstruction quality, and more specifically, with fixation-based saliency combined with the MAE strategy. By examining the emergent properties of representation learning and drawing parallels to human perceptual observation, we focus on how eye-tracking data informs the selection of image patches for reconstruction, aligning computational methods with human visual perception. Our findings reveal the potential of integrating eye-tracking insights to improve the accuracy and perceptual relevance of self-supervised learning models in computer vision. This study thus underscores the synergy between computational image restoration methods and human perception, facilitated by eye-tracking technology, opening new directions and insights for both fields. Our experiments are available for reproducibility in this GitHub Repository.

Webcam-based eye-tracking platforms have recently re-emerged due to improvements in machine learning-supported calibration processes and offer a scalable option for conducting eye movement studies. Although not yet comparable to the infrared-based ones regarding accuracy and frequency, some compelling performances have been observed, especially in those scenarios with medium-sized AOI (Areas of Interest) in images. In this study, we test the reliability of webcam-based eye-tracking on a specific task: Eye movement distribution analysis for CVD (Colour Vision Deficiency) detection. We introduce a new publicly available eye movement dataset based on a pilot study (n=12) on images with dominant red colour (previously shown to be difficult with dichromatic AOI to investigate CVD by comparing attention patterns obtained in webcam eye-tracking sessions). We hypothesized that webcam eye tracking without infrared support could detect differing attention patterns between CVD and non-CVD participants and observed statistically significant differences, allowing the retention of our hypothesis.

After demonstrating that rainbow colors are still commonly used in scientific publications, we comparatively evaluate the rainbow and sequential color schemes on choropleth and isarithmic maps in an empirical user study with 544 participants to examine if a) people intuitively associate order for the colors in these schemes, b) they can successfully conduct perceptual and semantic map reading and recall tasks with quantitative data where order may have implicit or explicit importance. We find that there is little to no agreement in ordering of rainbow colors while sequential colors are indeed intuitively ordered by the participants with a strong dark is more bias. Sequential colors facilitate most quantitative map reading tasks better than the rainbow colors, whereas rainbow colors competitively facilitate extracting specific values from a map, and may support hue recall better than sequential. We thus contribute to dark- versus light is more bias debate, demonstrate why and when rainbow colors may impair performance, and add further nuance to our understanding of this highly popular, yet highly criticized color scheme.

Gaze-Contingent Displays (GCDs) can improve visual search performance on large displays. GCDs, a Level Of Detail (LOD) management technique, discards redundant peripheral detail using various human visual perception models. Models of depth and contrast perception (e.g., depth-of-field and foveation) have often been studied to address the trade-off between the computational and perceptual benefits of GCDs. However, color perception models and combinations of multiple models have not received as much attention. In this paper, we present GeoGCD which uses individual contrast, color, and depth-perception models, and their combination to render scenes without perceptible latency. As proof-of-concept, we present a three-stage user evaluation built upon geographic image interpretation tasks. GeoGCD does not impair users' visual search performance or affect their display preferences. On the contrary, in some cases, it can significantly improve users' performance.

Visual analytics is often based on the intuition that highly interactive and dynamic depictions of complex and multivariate databases amplify human capabilities for inference and decision-making, as they facilitate cognitive tasks such as pattern recognition, association, and analytical reasoning (Thomas and Cook 2005 Thomas, J.J. and Cook, K.A. 2005. Illuminating the path: the research and development agenda for visual analytics, Los Alamitos, CA: IEE Computer Society Press. National Visualization and Analytics Ctr [Google Scholar]). But how do we know whether visual analytics really works? This article offers a generic evaluation approach combining theory- and data-driven methods based on sequence similarity analysis. The approach systematically studies users' visual interaction strategies when using highly interactive interfaces. We specifically ask whether the efficiency (i.e., speed) of users can be characterized by specific display interaction event sequences, and whether studying user strategies could be employed to improve the (interaction) design of the dynamic displays. We showcase our approach using a very large, fine-grained spatiotemporal dataset of eye movement recordings collected during a controlled human subject experiment with dynamic visual analytics displays. With this methodological approach based on empirical evidence, we hope to contribute to a deeper understanding of how people make inferences and decisions with highly interactive visualization tools and complex displays.

This paper presents a brief overview of space variant image coding for stereoscopic media and reports on findings from a study using foveation for stereoscopic imaging. Foveation is a perceptually motivated approach to image coding based on the structure of human fovea and it is well studied in image and video processing domains. However it is less exploited for three-dimensional (3D) space even though it is potentially well suited also for 3D, e.g. for level of detail management in gaze contingent stereoscopic displays. In this paper we present results from a stereoscopic foveation implementation to test this argument. A brief discussion on computational as well as human factors for successful management and presentation of stereoscopic media is also provided based on current literature.

Advancements in graphics hardware have allowed development of hardware-accelerated imaging displays. This article reviews techniques for real-time simulation of arbitrary visual fields over still images and video. The goal is to provide the vision sciences and perceptual graphics communities techniques for the investigation of fundamental processes of visual perception. Classic gaze-contingent displays used for these purposes are reviewed and for the first time a pixel shader is introduced for display of a high-resolution window over peripherally degraded stimulus. The pixel shader advances current state-of-the-art by allowing real-time processing of still or streamed images, obviating the need for preprocessing or storage.