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Creating Segments and Effects on Comics by Clustering Gaze Data

Creating Segments and Effects on Comics by Clustering Gaze Data Creating Segments and Effects on Comics by Clustering Gaze Data ISHWARYA THIRUNARAYANAN, KHIMYA KHETARPAL, and SANJEEV KOPPAL, University of Florida OLIVIER LE MEUR, IRISA University of Rennes 1 JOHN SHEA and EAKTA JAIN, University of Florida Traditional comics are increasingly being augmented with digital effects, such as recoloring, stereoscopy, and animation. An open question in this endeavor is identifying where in a comic panel the effects should be placed. We propose a fast, semi-automatic technique to identify effects-worthy segments in a comic panel by utilizing gaze locations as a proxy for the importance of a region. We take advantage of the fact that comic artists influence viewer gaze towards narrative important regions. By capturing gaze locations from multiple viewers, we can identify important regions and direct a computer vision segmentation algorithm to extract these segments. The challenge is that these gaze data are noisy and difficult to process. Our key contribution is to leverage a theoretical breakthrough in the computer networks community towards robust and meaningful clustering of gaze locations into semantic regions, without needing the user to specify the number of clusters. We present a method based on the concept of relative eigen quality that takes a http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
1551-6857
DOI
10.1145/3078836
Publisher site
See Article on Publisher Site

Abstract

Creating Segments and Effects on Comics by Clustering Gaze Data ISHWARYA THIRUNARAYANAN, KHIMYA KHETARPAL, and SANJEEV KOPPAL, University of Florida OLIVIER LE MEUR, IRISA University of Rennes 1 JOHN SHEA and EAKTA JAIN, University of Florida Traditional comics are increasingly being augmented with digital effects, such as recoloring, stereoscopy, and animation. An open question in this endeavor is identifying where in a comic panel the effects should be placed. We propose a fast, semi-automatic technique to identify effects-worthy segments in a comic panel by utilizing gaze locations as a proxy for the importance of a region. We take advantage of the fact that comic artists influence viewer gaze towards narrative important regions. By capturing gaze locations from multiple viewers, we can identify important regions and direct a computer vision segmentation algorithm to extract these segments. The challenge is that these gaze data are noisy and difficult to process. Our key contribution is to leverage a theoretical breakthrough in the computer networks community towards robust and meaningful clustering of gaze locations into semantic regions, without needing the user to specify the number of clusters. We present a method based on the concept of relative eigen quality that takes a

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)Association for Computing Machinery

Published: May 31, 2017

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