Visual saliency is a common computational method to detect attention-drawing regions in images, abiding by top-down and bottom-up processes of visual attention. Computer vision algorithms generate saliency maps, which often undergo a validation step in eye-tracking sessions with human participants in controlled labs. However, due to the covid-19 pandemic, experimental sessions have been difficult to roll out. Thus, new webcam-based tools, powered by the developments in machine learning, come into play to help track down onscreen eye movements. Claimed error rates of recent webcam eye trackers can be as low as 1.05°, comparable to sophisticated infrared-based eye-trackers, opening new paths to explore. Using webcams allows reaching a broader participant pool and collecting data over different experiments (e.g., free viewing or task-driven). In our work, we collect webcam eye-tracking data over a collection of images with 2-4 salient objects against a homogenous background. Objects within the images represent our AOIs (areas of interest). We have two main goals: a) Check how eye movements vary on AOIs across all spatial permutations of the same AOI in a given image; b) Extract correlations for a given image containing N objects between viewers’ eye movement dwell times over the N AOIs and the corresponding AOIs saliency maps. We will show relationships between viewers’ dwell time over each AOI throughout all factorial N spatial permutations and variance of AOIs’ salient pixels. Based on this relationship, eventually, object-oriented saliency models can be used to predict dwell-time distributions over AOIs for a given image. ________________________________ Alessandro Bruno PhD Lecturer in Computing Department of Computing and Informatics Faculty of Science and Technology [cid:bbd3c708-13de-4ca3-a69e-956741eded8f][cid:6908fc80-d78d-4e96-a884-c1e81b698e56] address: Talbot Campus Poole, BH12 5BB, United Kingdom website: https://staffprofiles.bournemouth.ac.uk/display/abruno email address: abruno@bournemouth.ac.uk orcid profile: http://orcid.org/0000-0003-0707-6131 Interested in publishing your work on mammographic image analysis? https://www.mdpi.com/journal/jimaging/special_issues/mammographic_image_analysis ________________________________ ________________________________ BU is a Disability Confident Employer and has signed up to the Mindful Employer charter. Information about the accessibility of University buildings can be found on the BU AccessAble webpages. This email is intended only for the person to whom it is addressed and may contain confidential information. If you have received this email in error, please notify the sender and delete this email, which must not be copied, distributed or disclosed to any other person. Any views or opinions presented are solely those of the author and do not necessarily represent those of Bournemouth University or its subsidiary companies. Nor can any contract be formed on behalf of the University or its subsidiary companies via email.