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.

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