本論文提出一個以類神經網路為基礎的印花織物電腦分色系統，此系統主要的功能是要將印花織物上彩色圖案的色彩精確區分出並進行對色以改善目前業者耗費工時的人工分色對色。所採用的色彩空間(Color Space)是以紅(Red, R)、綠(Green, G)、藍(Blue, B)等三軸(Three-axes)來呈現，也就是所謂的RGB色彩模式。本論文的分色系統區分為三個階段：首先，為了減少分色的運算負荷，利用基因演算法(Genetic Algorithm, GA)搜尋出與印花織物原影像有相同色彩分佈且面積較小的子影像(Sub-image)，以供給後續的分色演算法使用。接著第二階段的分色演算法上，本論文是以兩種監督式(Supervised)學習網路進行印花織物的RGB影像進行分色。第一種是以倒傳遞類神經網路(Back-propagation Neural Network, BPNN)進行印花織物RGB子影像的分色；第二種是以機率類神經網路(Probabilistic Neural Network, PNN)進行印花織物RGB子影像的分色。最後第三階段在對色(Color Matching)的方法上是利用PANTONE®標準色卡色票(Color Ticket)進行對色。由實驗結果顯示出這兩種分色系統均可以成功達成印花織物的分色和對色，但由於機率類神經網路結構的設計容易使其應用於印花織物的分色和對色結果更佳。

This thesis proposes a computerized color separation system for printed fabrics by using an approach of artificial neural network. This system can be applied accurately in color separations that use a scanner to obtain digitized color images in the RGB (Red, Green, Blue) mode. Next, a Genetic Algorithm (GA) is applied to search for smaller sub-images with the same color distribution of the original printed fabrics in order to proceed with the subsequent color separation algorithm. In respect to the color separation algorithm, this system can be operated by two supervised learning networks on the RGB image of the printed fabrics. First, the color separation is conducted by using the Back-propagation Neural Network (BPNN) on the RGB sub-images of the printed fabrics. Second, it is conducted by using the Probabilistic Neural Network (PNN) on the RGB sub-images of the printed fabrics. Finally, as for the color matching algorithm, it is proceeded with the color ticket of the PANTONE Textile Numbering System. According to the experimental results, these two kinds of color separation systems can successfully complete color separations and color matching for printed fabrics' images, and the PNN method is more suitable for color separations of printed fabrics.

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