目前關於刺繡織物自動化影像辨識與分類的相關論文很少，現今刺繡產業的前端打版作業仍大幅仰賴人力在電腦打版軟體中使用不同顏色仔細描繪不同區域中的圖樣及影像，所以一套能自動分辨顏色、區域及圖樣的影像分析系統，實是提升刺繡產業競爭力的關鍵技術。在本文中，我們使用了均值法、中央加權中值法與形態學運算等濾波技術來濾除刺繡織物表面結構的光影變化，接著運用基因演算法來區分具重複圖樣以及非重複圖樣之刺繡影像，若具重複圖樣，則在原始影像中搜尋與之相同顏色成份及空間結構，但影像大小大幅降低的子影像，此舉可大幅降低整張影像的運算負荷，期能達成線上即時系統要求的處理速度。對於非重複圖樣之刺繡影像，則運用離散小波轉換來取得低頻部分之子影像，此方法能保留重要影像特徵同時又能提升演算效率。不論具重複圖樣與否，取得子影像後，再利用聚類適切性指標來決定確切的分類數，之後再以加權模糊C均值法進行分色及區域分割。實驗證明對於重複與非重複圖樣彩色刺繡影像，本文提出的方法均能成功地分色及完成區域分割，且能獲致良好的結果。
　　刺繡織物與其他平面織物如：印花布、斜紋布等不同，其圖案具有厚度的陰影、紋理具有毛邊及空隙，非常難以濾波及辨識，是刺繡產業自動化的瓶頸所在。有鑑於此，本文提出一種紋理擬合法，其為一種非濾波式數位影像處理法，對於具多個單連通、單色、單一紋理的封閉區域刺繡影像，能快速完成刺繡織物之分色、區域分割及紋理模擬，且能在模擬整張影像的刺繡紋理後，輸出至螢幕或繪圖機，進行模擬結果與實物比對，以作為真正刺繡前的效果確認，或作為一種刺繡織物的廣義濾波器。本文首先運用均值、形態、中央加權中值等組合式濾波器移除刺繡布面的光影變化、坯布上的週期性暗點，以及具雜訊的紋理結構。再以WFCM進行分色並重組一維影像點以完成區域分割。本文的第二部分，運用紋理擬合法來辨識繡線顏色並擬合出整張影像的紋理圖案。藉由輸出至繪圖裝置的方式，可驗證整體紋理模擬的正確性及有效性。

Currently, there are very few literatures on automatic image recognition and classification of embroidery fabrics. In today’s embroidery industry, front-end pattern-making still relies greatly on labor, using pattern-making software to carefully depict patterns and images in different colors and regions. Hence, an image analysis system that can recognize colors, regions and patterns automatically is a critical technique of improving the competitiveness of the embroidery industry. In this dissertation, the mean filtering method, central weighted median (CWM) filtering method and morphological operation are employed to filter out the light variation on embroidery fabric surface structure, and apply Genetic Algorithm (GA) to distinguish images of repeat pattern embroidery from that of non-repeat pattern embroidery. If it is a repeat pattern, then a much smaller sized sub image would be searched in the original image for the same color components and spatial structure, which could lower the computing load of the entire image greatly and is expected to achieve the processing speed required in an online real-time system. As for non-repeat pattern embroidery images, discrete wavelet transform (DWT) is applied to acquire low-frequency sub images, which can retain important image features while improving the computing efficiency. Be it a repeat or non-repeat pattern, after obtaining sub images, Specific Criteria (SC) is used to determine the exact number of clustering, and the weighted fuzzy C-means method (WFCM) is employed to run color separation and region separation. The experiment proved that, in regard to the color embroidery images of repeat and non-repeat patterns, the method proposed in this dissertation succeeded in color and region separations with good result.
The embroidery fabric is different from other planar fabrics such as printed fabrics and twill fabrics. Because embroidery fabrics have inherent solid texture patterns, furry edges, voids and thickness shadows, it is very difficult to filter and simulate texture patterns and is the bottleneck for embroidery automation. In view of this, this dissertation proposes the texture fitting method (TFM). TFM is a kind of non-filtered digital image processing method. For embroidery fabrics full of multiple single-connected, single-color and single-texture closed regions, TFM can fast complete color and region separation and texture simulation, and then output the result to monitors or plotters to investigate the simulation effect and compare it to real fabrics, or use this technology as a generalized filter for embroidery fabrics. This dissertation first addresses a combination of mean, morphological and central weighted median filters to remove light variation on embroidery surface, periodic darkness on the greige and noised texture structures so as to separate colors by WFCM and reshape 1D image pixels to finish region separation. The second part of this dissertation utilizes TFM to identify stitch colors and simulate texture patterns over the whole image. By exporting the result to visual devices, we can prove the integral correctness and efficiency of the texture simulation.

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