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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭彥甫 | |
| dc.contributor.author | Tzu-Yuan Lin | en |
| dc.contributor.author | 林子淵 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:10:01Z | - |
| dc.date.available | 2023-08-19 | |
| dc.date.copyright | 2018-08-19 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-07-19 | |
| dc.identifier.citation | Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4, 40-79.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69170 | - |
| dc.description.abstract | 在台灣，每年有3千多隻的狗和貓走失，丟失寵物對於主人來說是非常難過的，這些走失的寵物也可能會對動物收容所造成負擔。儘管植入寵物晶片一直是解決走失寵物問題的方法之一，但它可能會給寵物帶來健康問題（例如發炎反應和癌症）。因此，我們需要一種非侵入式的走失寵物識別方法。本研究利用非侵入式的臉部辨識方法來識別個體貓，拍攝了一個包含150隻不同的個體貓、共900張貓照片的數據集，使用卷積神經網絡偵測臉上的五官（例如眼睛、鼻子和嘴巴）， 將五官的特徵利用特徵臉(Eigenface)量化後，使用支持向量機(Support Vector Machine)來進行辨識。本研究提出的方法達到了94.1％的識別準確度。 | zh_TW |
| dc.description.abstract | In Taiwan, there are more than 3 thousand dogs and cats missing every year. Losing pets could be extremely painful for owners. It also places burden on animal shelters in trying to return the pets to the owners. Although implanting microchips has always been a way to solve the missing pets problem, it may cause health problems (e.g., inflammatory reaction and cancer) to pets. Hence, a noninvasive approach for identifying missing pets is needed. This work proposed to identify cats noninvasively using face recognition. A database that contains 900 images of 150 different cats was developed. Facial parts (e.g., eyes, nose, and mouth) were identified using convolutional neural networks. The features of the facial parts (e.g., eigenface) were then qualified and were used for identifying the cats with support vector machines. The proposed method achieves an identification accuracy of 94.1 %.. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:10:01Z (GMT). No. of bitstreams: 1 ntu-107-R03631030-1.pdf: 2698502 bytes, checksum: a6a0fcf53e06c8a68aea7c9361fd302f (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | TABLE OF CONTENTS
ACKNOWLEDGEMENTS i 摘要 ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii CHAPTER 1. INTRODUCTION 1 1.1 Background 1 1.2 Objectives 1 1.3 Organization 2 CHAPTER 2. LITERATURE REVIEW 3 2.1 Traditional methods of cat face detection 3 2.2 Advanced detection approaches using deep learning 3 2.3 Conventional researches of cat identification 4 CHAPTER 3. MATERIALS AND METHODS 5 3.1 Dataset preparation 5 3.2 Face detection and facial parts localization 6 3.3 Face alignment 8 3.4 Facial feature extraction 9 3.5 Feature representation using Eigenface 10 3.6 Cat identification 11 CHAPTER 4. RESULTS 12 4.1 The loss of the face detection and facial parts localization models during training 12 4.2 The performance of the developed face detection and facial parts localization models 13 4.3 The performance of cat identification 15 CHAPTER 5. CONCLUSION 17 REFERENCES 18 | |
| dc.language.iso | en | |
| dc.subject | 貓 | zh_TW |
| dc.subject | 卷積神經網絡 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 人臉識別 | zh_TW |
| dc.subject | cat | en |
| dc.subject | Convolutional neural network | en |
| dc.subject | face recognition | en |
| dc.subject | support vector machine | en |
| dc.title | 利用深度學習辨識貓臉 | zh_TW |
| dc.title | Cat face recognition using deep learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭文皇,花凱龍,王尚麟 | |
| dc.subject.keyword | 卷積神經網絡,貓,人臉識別,支持向量機, | zh_TW |
| dc.subject.keyword | Convolutional neural network,cat,face recognition,support vector machine, | en |
| dc.relation.page | 20 | |
| dc.identifier.doi | 10.6342/NTU201801677 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-07-20 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物機電工程學系 | |
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| ntu-107-1.pdf 未授權公開取用 | 2.64 MB | Adobe PDF |
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