臺灣博碩士論文加值系統

本論文主要目的在探討螢光織物在不同照明環境下的色彩變化，及螢光織物在不同照明光下的色彩預測方法。為了達到此目的，本論文設計了兩個實驗，分別為實驗一：「螢光色色差評價實驗」及實驗二：「螢光色色彩預測實驗」。
實驗一：「螢光色色差評價實驗」為探討螢光紡織物在不同光源與紫外光結合的照射下所產生的色差，並進一步分析量測色差與感知色差兩者的關係，再利用色差評價結果分析不同色差公式在螢光紡織的PF/3數值及其相關係數。實驗二：「螢光色色彩預測實驗」提出一種將可見光反射與紫外光激發成分在參考環境下分離量測，再預測多種測試環境下螢光色彩LAB值的方法。
依實驗結果可知，在實驗一中，螢光紡織色彩會受照明光源色溫的影響，且受測者的評價相當穩定，具有一定的可信度。所有色差公式中，CIE94色差公式在本實驗中的誤差率相對較低。在實驗二中，可見光成分的色刺激預測方面，類神經網路明顯優於使用CIELAB參考白轉換或是Bradford色適應轉換。在紫外光激發成分方面，必須考量紫外線照度與螢光反應的非線性關係，才有較好的預測效果。

The main purpose of this study is to assess color change of fluorescent digital textile printing in different lighting environments, and to propose a color prediction method for fluorescent textile under different illuminations. To achieve this goal, two experiments are designed, namely Experiment 1: “Fluorescence Color Difference Evaluation Experiment” and Experiment 2: “Fluorescence Color Prediction Experiment”.
Experiment 1 investigates the color difference induced by adding ultraviolet light (UV) to standard light sources, and further analyze the relationship between the measured color differences and the perceived color differences, and then use the color difference evaluation. Results The results are analyzed by PF/3 values and correlation coefficients. Experiment 2 proposes a method to predict the flurescent effect under different illuminations by separating the visible light reflection component from the ultraviolet excitation component in a reference condition.
The results of the Experiment 1 shows that the color of the fluorescent textile is affected by color temperature of the light source, and the results are quite stable. CIE94 color difference formula is better than the other formulae in prediction the differences. The results of the Experiment 2 shows that artificial neural network can significantly improve the accuracy of color prediction of the reflection combonents compared to commonly used CIELAB reference white transform or Bradford color adaptation transform. In terms of ultraviolet excitation components, nonlinear relationship between ultraviolet illuminance and fluorescence response must be considered to achieve high accuracy of color prediction.

摘要 I
Abstract II
致謝 III
目錄 V
圖目錄 VIII
表目錄 XI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 論文架構 2
1.4 研究範圍與限制 3
第二章 原理介紹 4
2.1 光譜能量與色彩 4
2.2 色匹配實驗 5
2.3 色彩資訊轉換 6
2.4 CIE 色度座標 6
2.5 黑體輻射 8
2.6 黑體軌跡與色溫 9
2.7 CIE日光 11
2.8 CIE標準光源 12
2.9 色差公式 13
2.10 Bradford色適應公式 17
2.11 類神經網路 18
2.12 紡織數位印製技術 18
2.12.1 數位打樣 19
2.12.2 數位打樣色彩管理 20
2.13 心理物理評價技術 21
2.13.1 雙眼視覺觀察法 22
2.13.2 單眼視覺觀察法 23
2.13.3 記憶視覺評價法 23
第三章 文獻探討 25
3.1 紫外線輻射與安全防護 25
3.1.1 紫外線輻射 25
3.1.2 視覺上防護 27
3.1.3 UV紫外線人造光源 28
3.2 螢光墨水相關研究 29
第四章 研究方法 32
4.1 研究架構 32
4.2 研究儀器與設備 33
4.3 使用方法介紹 34
4.3.1 光譜與色彩量測 34
4.3.2 光源條件 35
4.3.3 視覺判斷標準 35
4.4 研究分析方法 37
4.4.1 相關係數分析 37
4.4.2 效能因子分析 38
4.4.3 變異係數 38
4.4.4 受測者的分析 39
4.4.5 色差公式與感知色差的分析 39
第五章 螢光色色差評價實驗 40
5.1 實驗設計 40
5.2 實驗環境 41
5.3 色差評價實驗建立 44
5.4 色差評價實驗流程 49
5.5 實驗結果與分析 51
5.5.1 受測者分析 51
5.5.2 紙質樣本評價結果 52
5.5.3 螢光紡織評價結果 57
5.6 小結 61
第六章 螢光色色彩預測實驗 63
6.1 實驗設計 63
6.2 實驗環境 66
6.2.1 紫外線頻譜的選擇 66
6.2.2 量測儀器的選擇 67
6.3 利用類神經網路預測不同光源下的LAB值 70
6.4 實驗結果與分析 75
6.4.1 神經網路預測分析 75
6.4.2 螢光色彩預測結果分析 78
6.4.3 螢光色色彩預測結果模擬 86
6.5 小結 88
第七章 結論與未來課題 89
參考文獻 91
附錄一 研究儀器與設備 97
附錄二 螢光色彩預測實驗數據 100

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