國立虎尾科技大學 |

Modeling Eye Tracking Data with Application to Object Detection.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Modeling Eye Tracking Data with Application to Object Detection./
Author:	Shanmuga Vadivel, Karthikeyan.
Description:	1 online resource (177 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9781321568608

Modeling Eye Tracking Data with Application to Object Detection.
Shanmuga Vadivel, Karthikeyan.

Modeling Eye Tracking Data with Application to Object Detection. - 1 online resource (177 pages)

Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.

Thesis (Ph.D.)--University of California, Santa Barbara, 2014.

Includes bibliographical references

This research focuses on enhancing computer vision algorithms using eye tracking and visual saliency. Recent advances in eye tracking device technology have enabled large scale collection of eye tracking data, without affecting viewer experience. As eye tracking data is biased towards high level image and video semantics, it provides a valuable prior for object detection in images and object extraction in videos. We specifically explore the following problems in the thesis: 1) eye tracking and saliency enhanced object detection, 2) eye tracking assisted object extraction in videos, and 3) role of object co-occurrence and camera focus in visual attention modeling.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321568608Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Modeling Eye Tracking Data with Application to Object Detection.
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020 $a 9781321568608
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100 1 $a Shanmuga Vadivel, Karthikeyan. $3 1186732
245 1 0 $a Modeling Eye Tracking Data with Application to Object Detection.
264 0 $c 2014
300 $a 1 online resource (177 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
500 $a Adviser: B.S. Manjunath.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2014.
504 $a Includes bibliographical references
520 $a This research focuses on enhancing computer vision algorithms using eye tracking and visual saliency. Recent advances in eye tracking device technology have enabled large scale collection of eye tracking data, without affecting viewer experience. As eye tracking data is biased towards high level image and video semantics, it provides a valuable prior for object detection in images and object extraction in videos. We specifically explore the following problems in the thesis: 1) eye tracking and saliency enhanced object detection, 2) eye tracking assisted object extraction in videos, and 3) role of object co-occurrence and camera focus in visual attention modeling.
520 $a Since human attention is biased towards faces and text, in the first work we propose an approach to isolate face and text regions in images by analyzing eye tracking data from multiple subjects. Eye tracking data is clustered and region labels are predicted using a Markov random field model. In the second work, we study object extraction in videos using eye tracking prior. We propose an algorithm to extract dominant visual tracks in eye tracking data from multiple subjects by solving a linear assignment problem. Visual tracks localize object search and we propose a novel mixed graph association framework, inferred by binary integer linear programming. In the final work, we address the problem of predicting where people look in images. We specifically explore the importance of scene context in the form of object co-occurrence and camera focus. The proposed model extracts low-, mid- and high-level and scene context features and uses a regression framework to predict visual attention map. In all the above cases, extensive experimental results show that the proposed methods outperform current state-of-the-art.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0544
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Santa Barbara. $b Electrical and Computer Engineering. $3 1186733
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3682975 $z click for full text (PQDT)