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Luận văn thạc sĩ Khoa học máy tính:  Phân đoạn ảnh tuyến tụy bằng phương pháp học sâu

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Thông tin cơ bản

Tiêu đề Phân đoạn ảnh tuyến tụy bằng phương pháp học sâu
Tác giả Vế Nguyễn Thành
Trường học Trường Đại học Bách Khoa
Chuyên ngành Khoa học máy tính
Thể loại Luận văn thạc sĩ
Năm xuất bản 2019
Thành phố Thành phố Hồ Chí Minh
Định dạng
Số trang 46
Dung lượng 1,03 MB

Nội dung

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2.1.4 SIFT

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x ;iFÿӏQKNH\SRLQWWҥLPӛLYӏWUtPӝWP{KuQKFKLWLӃWÿѭӧFiSGөQJÿӇ[iFÿӏQKYӏWUtYjWӍOӋ&iFNH\SRLQWÿѭӧFFKӑQGӵDWUrQYLӋFÿROѭӡQJWtQKәQÿӏQKFӫDFK~QJ

x ;iF ÿӏQK Orientation PӝW KRһF QKLӅX orientation ÿѭӧF JiQ FKR PӛLkeypoint GӵD WUrQ LPDJH JUDGLHQW 7ҩW Fҧ FiF WtQK WRiQ WURQJ WѭѫQJ ODLWKӵFKLӋQWUrQGӳOLӋXҧQKÿmÿѭӧFFKX\ӇQÿәLVDQJ orientation, scale và location FӫDWӯQJ feature

x Keypoint descriptor: image gradient ÿѭӧF ÿR WKHR WӍ OӋ ÿm FKӑQ WURQJYQJ [XQJ TXDQK NH\SRLQW &K~QJ ÿҥL GLӋQ FKR PӭF ÿӝ ELӃQ GҥQJ YjWKD\ÿәLÿӝsáng

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2.1.5 Random Forest

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x 7KrPKjPQJүXQKLrQYjRTXiWUuQKKXҩQOX\ӋQFk\

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2.1.6 Precision và Recall

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7KӵFWӃ'ѭѫQJWtQK Âm tính

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&{QJWUuQKFӫD:DQJÿӅ[XҩWPӝWSKѭѫQJSKiSODQJWUX\ӅQQKmQWUrQFiFWLӇXYQJVӱGөQJrelative geodesic distances&iFKWLӃSFұQQj\ÿѭӧFFRLOjVӵNKӣLÿҫXÿӇSKiWWULӇQFiFSKѭѫQJSKiSSKkQÿRҥQҧQKERWWRP-up

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3.1 <Ç8&Ҫ8%¬,72È1

3.1.1 'ӳOLӋXÿҫXYjR

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ӬQJYӟLPӛL ҧQK'&7ÿҫX vào, P{KuQKVӁFKRUDPӝWWұSWLQD Yӟi cùng ÿӝSKkQJLҧLWұSWLQQj\FKӭDFiFSL[HOWѭѫQJӭQJYӟLҧQK'&7 ÿҫXYjRQKѭQJJLiWUӏPӛLSL[HOOjKRһFWѭѫQJӭQJYӟLFiFQKmQ³WX\ӃQWө\´KRһF³NK{QJWX\ӃQWө\´

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ĈӇJLҧLTX\ӃWEjLWRiQSKkQÿRҥQҧQKWX\ӃQWө\WiFJLҧ iSGөQJ SKѭѫQJSKiSWLӃSFұQVӱGөQJFKLӃQOѭӧFERWWRP-up [22] YӟLP{KuQKKRҥWÿӝQJÿѭӧFPLQKKӑDӣHình 3.1 0ӛL ҧQK&7FҳWOӟSÿҫXYjRVӁÿѭӧFSKkQ ÿRҥQWUѭӟFGӵDWUrQÿyPӝW

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