{"_buckets": {"deposit": "2b2aa364-b2eb-4684-8138-0e99ddd42930"}, "_deposit": {"id": "3469", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3469"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3469", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Feature Based Myanmar Fingerspelling Image Classification Using SIFT, SURF and BRIEF", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Deaf people use Sign Language andFingerspelling as a fundamental communicationmethod. Fingerspelling or manual spelling is amethod of spelling words using hand movements, andmost often used to spell out names of people, places,organizations, books and other words for which nosign exists. In this experiment, the images for 31 staticfingerspelling characters of Myanmar consonant areused as the input images. Three feature vectorsextraction methods (SIFT, SURF, and BRIEF) weredone separately on our collected Myanmar SignLanguage (MSL) fingerspelling images. MSLfingerspelling data are classified with seven differentapproaches; Multilayer Perceptron, Gaussian NaïveBays, Decision Tree, Logistic Regression, RandomForest, Support Vector Machine and K-NearestNeighbor. In this paper, we provide the performanceresults of different features on different classifiers andthe highest classification rate is up to 97% with SURFfeature and Random Forest classifier. Moreover, 10-fold cross validation was made in our experiment andwe provide the classification results for eachclassifier."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-23"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "ICCA 2019 Proceedings Book-pages-256-264.pdf", "filesize": [{"value": "842 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 842000.0, "url": {"url": "https://meral.edu.mm/record/3469/files/ICCA 2019 Proceedings Book-pages-256-264.pdf"}, "version_id": "fc554508-dd9a-4c10-baa8-b8f448872798"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Seventeenth International Conference on Computer Applications(ICCA 2019)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Aung, Ni Htwe"}, {"subitem_authors_fullname": "Thu, Ye Kyaw"}, {"subitem_authors_fullname": "Maung, Su Su"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2019-02-27"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/1216"}, "item_title": "Feature Based Myanmar Fingerspelling Image Classification Using SIFT, SURF and BRIEF", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003469", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-23"}, "publish_date": "2019-07-23", "publish_status": "0", "recid": "3469", "relation": {}, "relation_version_is_last": true, "title": ["Feature Based Myanmar Fingerspelling Image Classification Using SIFT, SURF and BRIEF"], "weko_shared_id": -1}