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研究生: 蔡智先
Chih-Hsien Tsai
論文名稱: 基於卷積神經網路之瞳孔尺寸辨識技術
Pupil Size Prediction Techniques Based on Convolution Neural Network
指導教授: 黃忠偉
Jong-Woei Whang
陳怡永
Yi-Yung Chen
口試委員: 陳怡永
Yi-Yung Chen
林瑞珠
Jui-Chu Lin
王孔政
Kung-Jeng Wang
林保宏
Pao-hung Lin
學位類別: 碩士
Master
系所名稱: 電資學院 - 光電工程研究所
Graduate Institute of Electro-Optical Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 65
中文關鍵詞: 深度學習瞳孔大小卷積神經網路多孔卷積
外文關鍵詞: Deep Learning, Pupil Size, Convolutions Neural Network, Dilated Convolutions
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  • 中文摘要 I Abstract II 誌謝 III 目錄 IV 圖目錄 VI 表目錄 VIII 第1章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 論文架構 4 第2章 眼部科學 5 2.1 人眼的構造 5 2.1.1 眼球的生理解剖 5 2.1.2 視網膜構造 6 2.1.3 視覺傳導路徑 8 2.2 瞳孔生理機制 9 第3章 深度學習 11 3.1 人工智慧與深度學習 11 3.2 卷積層(Convolutional Layer) 12 3.2.1 多孔卷積(Dilation Convolution) 14 3.3 池化層(Pooling Layer) 15 3.4 攤平層(Flatten Layer) 16 3.5 完全連結層(Fully Connected Layer) 16 3.6 激活函數(Activation Function) 17 3.7 輸出層(Output Layer) 18 3.8 損失函數(Loss Function) 19 3.9 模型擬合 19 第4章 資料集和方法 22 4.1 資料集 22 4.1.1 結構相似度係數(Structural Similarity Index, SSIM) 23 4.1.2 資料集之標注方法 24 4.2 網路結構與訓練方法 25 4.2.1 網路結構 25 4.2.2 訓練方法 26 第5章 實驗結果與討論 27 5.1 實驗結果 27 5.2 討論 30 5.2.1 模型效能評估 30 5.2.2 模型速度趨勢 31 5.2.3 模型比較 32 5.2.4 模型修正 33 第6章 結論與未來展望 35 6.1 結論 35 6.2 未來展望 36 參考文獻 37 附錄 41

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