{"created":"2021-01-11T04:38:59.790691+00:00","id":7366,"links":{},"metadata":{"_buckets":{"deposit":"246530e4-47f5-4c5b-a78f-f54f3326fba0"},"_deposit":{"created_by":71,"id":"7366","owner":"71","owners":[71],"owners_ext":{"displayname":"Kay_Thwe","email":"kay_thwe_kywe_aye@miit.edu.mm","username":"kay_thwe"},"pid":{"revision_id":0,"type":"depid","value":"7366"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/00007366","sets":["1582963674932","1582963674932:1597397050488"]},"communities":["miit"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Fruit Recognition Using Color and Morphological Feature Fusion","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"t is still difficult to recognize the kind of fruit\nwhich are of different colors, shapes, and textures. This\npaper proposes a features fusion method to recognize five\ndifferent classes of fruits that are the images from the\nfruit360 dataset. We are processed with four stages: preprocessing, boundary extraction, feature extractions, and\nclassification. Pre-processing is performed to remove the\nnoise by using the median filter, and boundary extraction\nare operated with the morphological operation. In feature\nextraction, we have extracted two types of features: color,\nand morphological features of the image. Color features\nare extracted from the RGB color channel, and\nmorphological features are extracted from the image that\ndetected the boundary of fruit by using morphological\noperations. These two types of features are combined in a\nsingle feature descriptor. These features are passed to\nfive different classifiers: Naïve Bayes (NB), Logistic\nRegression (LR), Support Vector Machine (SVM),\nDecision Tree (DT), K-Nearest Neighbor (KNN), and\nRandom Forest (RF). In the study, the accuracy that\nclassified with Random Forest (RF) classifier for the\nproposed feature fusion method is better than the other\nclassifiers, such as Naïve Bayes (NB), Logistic\nRegression (LR), Support Vector Machine (SVM),\nDecision Tree (DT), K-Nearest Neighbor (KNN)."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Fruit recognition, feature fusion, color feature, morphological feature"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2021-01-11"}],"displaytype":"preview","filename":"Fruit Recognition Using Color and Morphological Feature Fusion.pdf","filesize":[{"value":"541 KB"}],"format":"application/pdf","licensetype":"license_0","url":{"url":"https://meral.edu.mm/record/7366/files/Fruit Recognition Using Color and Morphological Feature Fusion.pdf"},"version_id":"751d0eae-bed8-4e01-81ff-dd8c5791f1e9"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"10","subitem_journal_title":"International Journal of Image, Graphics and Signal Processing (IJIGSP)","subitem_pages":"8-15","subitem_volume":"Vol-11"}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Myint San"},{"subitem_authors_fullname":"Mie Mie Aung"},{"subitem_authors_fullname":"Phyu Phyu Khaing"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Journal article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2019-10-11"},"item_title":"Fruit Recognition Using Color and Morphological Feature Fusion","item_type_id":"21","owner":"71","path":["1582963674932","1597397050488"],"publish_date":"2021-01-11","publish_status":"0","recid":"7366","relation_version_is_last":true,"title":["Fruit Recognition Using Color and Morphological Feature Fusion"],"weko_creator_id":"71","weko_shared_id":-1},"updated":"2022-03-24T23:15:25.249331+00:00"}