Leveraging gaze data for segmentation and effects on comics

Abstract

In this work, we present a semi-automatic method based on gaze data to identify the objects in comic images on which digital effects will look best. Our key contribution is a robust technique to cluster the noisy gaze data without having to specify the number of clusters as input. We also present an approach to segment the identified object of interest.