國立虎尾科技大學 |

RGB Image-Based Pupillary Diameter Tracking with Deep Convolutional Neural Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	RGB Image-Based Pupillary Diameter Tracking with Deep Convolutional Neural Networks./
作者:	Wangwiwattana, Chatchai.
面頁冊數:	1 online resource (152 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355528176

RGB Image-Based Pupillary Diameter Tracking with Deep Convolutional Neural Networks.
Wangwiwattana, Chatchai.

RGB Image-Based Pupillary Diameter Tracking with Deep Convolutional Neural Networks. - 1 online resource (152 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--Southern Methodist University, 2017.

Includes bibliographical references

Pupillary diameter monitoring has proven successful at objectively measuring cognitive load. This work presents three robust RGB video based pupillary diameter trackers and compares them for measuring cognitive load using commodity cameras. We investigate the use of modified starburst algorithm from previous work and propose two algorithms: 2-Level Snakuscules and a convolutional neural network which we call PupilNet. Our results show PupilNet outperforms other algorithms in measuring pupil dilation, is robust to various lighting conditions, and robust to different eye colors. In addition, this work explores various deep learning architecture for extracting the pupillary response as well as understand the network's behavior with three visualization techniques: feature and filter map visualization, gradient ascent, and occluded heat map. The study shows promising results for extracting the pupillary response as objective cognitive load measurement.

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

Mode of access: World Wide Web

ISBN: 9780355528176Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

RGB Image-Based Pupillary Diameter Tracking with Deep Convolutional Neural Networks.
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500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Eric C. Larson.
502 $a Thesis (Ph.D.)--Southern Methodist University, 2017.
504 $a Includes bibliographical references
520 $a Pupillary diameter monitoring has proven successful at objectively measuring cognitive load. This work presents three robust RGB video based pupillary diameter trackers and compares them for measuring cognitive load using commodity cameras. We investigate the use of modified starburst algorithm from previous work and propose two algorithms: 2-Level Snakuscules and a convolutional neural network which we call PupilNet. Our results show PupilNet outperforms other algorithms in measuring pupil dilation, is robust to various lighting conditions, and robust to different eye colors. In addition, this work explores various deep learning architecture for extracting the pupillary response as well as understand the network's behavior with three visualization techniques: feature and filter map visualization, gradient ascent, and occluded heat map. The study shows promising results for extracting the pupillary response as objective cognitive load measurement.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a Southern Methodist University. $b Computer Science and Engineering. $3 1184430
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