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...
| Main Authors: | , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/13449/ http://ir.unimas.my/id/eprint/13449/1/Measuring%20Task%20Performance%20Using%20Gaze%20Regions%20%28abstract%29.pdf |
| _version_ | 1848837411797204992 |
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| author | Irwandi, Hipiny Hamimah, Ujir |
| author_facet | Irwandi, Hipiny Hamimah, Ujir |
| author_sort | Irwandi, Hipiny |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T06:39:14Z |
| format | Proceeding |
| id | unimas-13449 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:39:14Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-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 Proceeding 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. |
| spellingShingle | T Technology (General) Irwandi, Hipiny Hamimah, Ujir Measuring Task Performance Using Gaze Regions |
| title | 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_short | Measuring Task Performance Using Gaze Regions |
| title_sort | measuring task performance using gaze regions |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/13449/ http://ir.unimas.my/id/eprint/13449/1/Measuring%20Task%20Performance%20Using%20Gaze%20Regions%20%28abstract%29.pdf |