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研究生:竺君儫
研究生(外文):Chu, Chun-Hao
論文名稱:數位色彩與影像技術應用於視光學之研究
論文名稱(外文):Digital Color and Spectral Imaging for Optometry Applications
指導教授:田仲豪
指導教授(外文):Tien, Chung-Hao
口試委員:陳政寰歐陽盟楊宗勳
口試委員(外文):Chen, Cheng HuanOu, Yang-MengYang, Tsung-Hsun
口試日期:2017-09-13
學位類別:碩士
校院名稱:國立交通大學
系所名稱:光電工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:中文
論文頁數:53
中文關鍵詞:線性代數色彩學主成分分析法反射頻譜估計虹膜顏色叢發性頭痛
外文關鍵詞:linear algebraColorimetryprincipal component analysisspectral reflectance estimationiris colorcluster headache
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在本論文中,我們探討了色彩以及頻譜資訊之間的空間轉換關係。我們利用主成分分析法將高維度的頻譜空間投影並壓縮至三維色彩空間的同時,並保留大部分的資訊量。以虹膜頻譜影像為例,由於人眼無法用於過高的曝光條件,我們捨棄傳統利用空間或時間掃描的機制,利用主成分分析法與最小平方差數位技術將虹膜的色彩資訊經由偽逆矩陣重建其頻譜影像。此外,因為虹膜色彩具有多基因遺傳特徵,我們在本篇論文之中提出利用虹膜的色彩資訊對於頭痛病徵之關聯進行數據分析。我們試圖運用機器學習的方式對於叢發性頭痛患者與正常人進行分類。透過邏輯回歸訓練出來的模型,對於初步收集(27位叢發性頭痛患者)的頭痛資料進行分類,可達70%分類的預測成功率。
In this work, we studied the correspondence between the spectra and color infor-mation. Through the principal component analysis (PCA), we found the high dimensional spectra space can be compressed to the three dimensional color space while keeping most information. For the purpose of optometry, where the strong illumination is inappropriate for the iris imaging. We successfully reconstruct the spectral iridal imaging through the color information by mean of the PCA and least square approximation. In additional to the spectral estimation, this thesis also pioneered the possibility of head-ache classification through the color information. Since the iris color was known as an inherited trait via multiple genes. We tried to use machine learning to distinguish the cluster headache patients from the normal subjects. With preliminary headache data-base, 70% accuracy was achieved to classify the cluster patients and normal person.

中文摘要 Abstract (Chinese) iv
英文摘要 Abstract (English) v
致謝 Acknowledgement vi
目錄 Table of Contents vii
圖目錄 List of Figures ix
1. 緒論 1
1.1. 研究背景與動機 1
1.2. 研究目的 3
1.3. 論文架構 5
2. 色彩學 6
2.1. 色彩學基礎原理 6
2.1.1. 色彩的物理性質 6
2.1.2. 人眼視覺之色彩模型 8
2.2. CIE表色系統 10
2.2.1. CIE 1931色彩空間 10
2.2.2. YCbCr色彩空間 13
2.2.3. CIE 1976 L*a*b*色彩空間 14
2.2.4. 同色異譜 (metamerism) 16
3. 全數位頻譜重建系統 17
3.1. 主成分分析法(PCA) 17
3.1.1. 主成分分析法理論推導 17
3.1.2. 主成分分析法應用於頻譜分析 19
3.2. 多頻譜影像系統 21
3.2.1. 系統量測架構 23
3.2.2. 數位重建系統之數學模型 27
3.3. 色彩校正 30
3.3.1. 色彩校正理論 30
3.3.2. 色彩校正數學模型 31
4. 數位重建虹膜頻譜影像技術 33
4.1. 以色票作為訓練樣本之模型 33
4.1.1. 色票頻譜分析 33
4.1.2. 重建虹膜頻譜 35
4.2. 以虹膜頻譜作為訓練樣本之模型 37
4.2.1. 虹膜頻譜分析 37
4.2.2. 重建虹膜頻譜 39
4.3. 重建虹膜頻譜在各波段之影像 40
5. 多頻譜影像之應用:以診斷原發性頭痛為例 42
5.1. 以頻譜資訊分析虹膜 42
5.1.1. 實驗流程 42
5.1.2. 分析結果 43
5.2. 以數位三通道資訊值分析虹膜頻譜 44
5.2.1. 實驗流程 45
5.2.2. 分析結果 45
6. 結論與未來工作展望 48
6.1. 結論 48
6.2. 未來工作展望 50
7. 參考資料 51
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