Versatile Gaze Tracking Approach

Abstract

This research is aimed at the development of a versatile tool that couples gaze tracking technology with image matching techniques in an efficient manner. The developed software can easily identify and highlight areas on a computer screen that a user is focusing on. The tool effectively handles the zooming feature through optimized feature matching and template matching techniques. It has diverse applications, and its utility is tested via experiments on histopathology slides and PDF documents. Annotating large medical datasets manually is a lethargic and time-consuming process but such properly annotated datasets are vital for creating accurate machine learning models. This dissertation addresses this challenge by streamlining the annotation process. Experiments performed on histopathology images reveal that the software correctly identifies areas of the medical slides that are of interest with a high accuracy. It highlights these regions through a heatmap so that they can be easily annotated or tagged. Similarly, experiments performed on PDF documents showcase that the tool can easily detect the text that is being read by the user. Overall, feature matching outperforms template matching in both kinds of experiments by achieving an accuracy, precision and F1-score of over 0.9. Beyond medical imaging and assistance in data annotation, the tool has potential for a wide range of applications. It can be used for detecting bias in the review of academic papers. Moreover, it can provide insights into user behavior and preferences that can help in the formulation of effective marketing strategies. Thus, the tool can have numerous applications as it seamlessly blends gaze tracking technology with image matching algorithms.