Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition
This work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition, namely, motion representation through motion estimation and motion representation through motion residuals. In the former, motion estimation is used to compute the motion vectors...
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| Format: | article |
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2007
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| Online Access: | http://hdl.handle.net/11073/21362 |
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| _version_ | 1864513436719775744 |
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| author | Shanableh, Tamer |
| author2 | Assaleh, Khaled |
| author2_role | author |
| author_facet | Shanableh, Tamer Assaleh, Khaled |
| author_role | author |
| dc.creator.none.fl_str_mv | Shanableh, Tamer Assaleh, Khaled |
| dc.date.none.fl_str_mv | 2007 2021-03-11T09:35:31Z 2021-03-11T09:35:31Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Shanableh, T., Assaleh, K. Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition. J Image Video Proc 2007, 087929 (2007). https://doi.org/10.1155/2007/87929 1687-5281 http://hdl.handle.net/11073/21362 10.1155/2007/87929 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Springer |
| dc.relation.none.fl_str_mv | https://doi.org/10.1155/2007/87929 https://link.springer.com/article/10.1155/2007/87929 |
| dc.subject.none.fl_str_mv | Frequency Domain Feature Vector Feature extraction Markov Model Classification Accuracy |
| dc.title.none.fl_str_mv | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition, namely, motion representation through motion estimation and motion representation through motion residuals. In the former, motion estimation is used to compute the motion vectors of a video-based deaf sign or gesture. In the preprocessing stage for feature extraction, the horizontal and vertical components of such vectors are rearranged into intensity images and transformed into the frequency domain. In the second approach, motion is represented through motion residuals. The residuals are then thresholded and transformed into the frequency domain. Since in both approaches the temporal dimension of the video-based gesture needs to be preserved, hidden Markov models are used for classification tasks. Additionally, this paper proposes to project the motion information in the time domain through either telescopic motion vector composition or polar accumulated differences of motion residuals. The feature vectors are then extracted from the projected motion information. After that, model parameters can be evaluated by using simple classifiers such as Fisher's linear discriminant. The paper reports on the classification accuracy of the proposed solutions. Comparisons with existing work reveal that up to 39% of the misclassifications have been corrected. |
| format | article |
| id | aus_45e111fb9959e7052088b9374c0557ec |
| identifier_str_mv | Shanableh, T., Assaleh, K. Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition. J Image Video Proc 2007, 087929 (2007). https://doi.org/10.1155/2007/87929 1687-5281 10.1155/2007/87929 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21362 |
| publishDate | 2007 |
| publisher.none.fl_str_mv | Springer |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language RecognitionShanableh, TamerAssaleh, KhaledFrequency DomainFeature VectorFeature extractionMarkov ModelClassification AccuracyThis work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition, namely, motion representation through motion estimation and motion representation through motion residuals. In the former, motion estimation is used to compute the motion vectors of a video-based deaf sign or gesture. In the preprocessing stage for feature extraction, the horizontal and vertical components of such vectors are rearranged into intensity images and transformed into the frequency domain. In the second approach, motion is represented through motion residuals. The residuals are then thresholded and transformed into the frequency domain. Since in both approaches the temporal dimension of the video-based gesture needs to be preserved, hidden Markov models are used for classification tasks. Additionally, this paper proposes to project the motion information in the time domain through either telescopic motion vector composition or polar accumulated differences of motion residuals. The feature vectors are then extracted from the projected motion information. After that, model parameters can be evaluated by using simple classifiers such as Fisher's linear discriminant. The paper reports on the classification accuracy of the proposed solutions. Comparisons with existing work reveal that up to 39% of the misclassifications have been corrected.Springer2021-03-11T09:35:31Z2021-03-11T09:35:31Z2007Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfShanableh, T., Assaleh, K. Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition. J Image Video Proc 2007, 087929 (2007). https://doi.org/10.1155/2007/879291687-5281http://hdl.handle.net/11073/2136210.1155/2007/87929en_UShttps://doi.org/10.1155/2007/87929https://link.springer.com/article/10.1155/2007/87929oai:repository.aus.edu:11073/213622024-08-22T12:08:45Z |
| spellingShingle | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition Shanableh, Tamer Frequency Domain Feature Vector Feature extraction Markov Model Classification Accuracy |
| status_str | publishedVersion |
| title | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition |
| title_full | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition |
| title_fullStr | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition |
| title_full_unstemmed | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition |
| title_short | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition |
| title_sort | Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition |
| topic | Frequency Domain Feature Vector Feature extraction Markov Model Classification Accuracy |
| url | http://hdl.handle.net/11073/21362 |