Measuring Task Performance Using Gaze Regions
We present a novel method for measuring task performance using gaze regions, i.e., scene regions fixated by a subject as he or she performs a familiar manual task. The scene regions are learned as a bag of features representation, using library lookup based on the Histogram of Oriented Gradients fea...
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2015
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Online Access: | http://ir.unimas.my/id/eprint/13449/1/Measuring%20Task%20Performance%20Using%20Gaze%20Regions%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/13449/ |
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my.unimas.ir.134492017-02-14T07:28:23Z http://ir.unimas.my/id/eprint/13449/ Measuring Task Performance Using Gaze Regions Irwandi, Hipiny Hamimah, Ujir T Technology (General) We present a novel method for measuring task performance using gaze regions, i.e., scene regions fixated by a subject as he or she performs a familiar manual task. The scene regions are learned as a bag of features representation, using library lookup based on the Histogram of Oriented Gradients feature descriptor [1]. By establishing a set of task-specific exemplar models, i.e., models sourced from Pareto optimal sequences, the approach recognizes the local optima within a set of task-specific unlabeled models by estimating the distance (of each unlabeled model) to the exemplar models. During testing, the method is evaluated against a dataset of egocentric sequences, each containing gaze data, belonging to three manual skill-based activities. The results show perfect classification’s accuracy on several proposed schemes. 2015 Conference or Workshop Item PeerReviewed text en http://ir.unimas.my/id/eprint/13449/1/Measuring%20Task%20Performance%20Using%20Gaze%20Regions%20%28abstract%29.pdf Irwandi, Hipiny and Hamimah, Ujir (2015) Measuring Task Performance Using Gaze Regions. In: 2015 9th International Conference on IT in Asia (CITA) : Transforming Big Data into Knowledge, 4-5 August 2015, Kuching, Sarawak Malaysia. |
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T Technology (General) Irwandi, Hipiny Hamimah, Ujir Measuring Task Performance Using Gaze Regions |
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We present a novel method for measuring task performance using gaze regions, i.e., scene regions fixated by a subject as he or she performs a familiar manual task. The scene regions are learned as a bag of features representation, using library lookup based on the Histogram of Oriented Gradients feature descriptor [1]. By establishing a set of task-specific exemplar models, i.e., models sourced from Pareto optimal sequences, the approach recognizes the local optima within a set of task-specific unlabeled models by estimating the distance (of each unlabeled model) to the exemplar models. During testing, the method is evaluated against a dataset of egocentric sequences, each containing gaze data, belonging to three manual skill-based activities. The results show perfect classification’s accuracy on several proposed schemes. |
format |
Conference or Workshop Item |
author |
Irwandi, Hipiny Hamimah, Ujir |
author_facet |
Irwandi, Hipiny Hamimah, Ujir |
author_sort |
Irwandi, Hipiny |
title |
Measuring Task Performance Using Gaze Regions |
title_short |
Measuring Task Performance Using Gaze Regions |
title_full |
Measuring Task Performance Using Gaze Regions |
title_fullStr |
Measuring Task Performance Using Gaze Regions |
title_full_unstemmed |
Measuring Task Performance Using Gaze Regions |
title_sort |
measuring task performance using gaze regions |
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2015 |
url |
http://ir.unimas.my/id/eprint/13449/1/Measuring%20Task%20Performance%20Using%20Gaze%20Regions%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/13449/ |
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13.188404 |