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研究生: 邵森蘭
Sen-Lan Shau
論文名稱: 應用Skeletonization與Backpropagation Neural Network於電腦刺繡製版之研究
Automatic Embroidery Digitizing Based on Skeletonization and Backpropagation Neural Network
指導教授: 李忠謀
Lee, Chung-Mou
學位類別: 碩士
Master
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
畢業學年度: 87
語文別: 中文
論文頁數: 77
中文關鍵詞: 刺繡電腦製版中心軸逆傳逆網路曲線逼近
英文關鍵詞: embroidery, digitizing, skeletonization, backpropagation network, curve fitting
論文種類: 學術論文
相關次數: 點閱:513下載:0
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  • 電腦刺繡製版的目的是將設計圖轉換成電子訊號,用以控制繡花機。目前的製版方法尚仰賴傳統人工作業,須耗費大量人力且十分費時。
    本研究藉由圖學的理論與影像處理的技術,提出電腦刺繡製版的流程與模式。研究範圍鎖定英文字母與阿拉伯數字。先萃取影像之中心軸、分枝點與端點等特徵以表示刺繡樣本,以上述特徵規劃刺繡時針頭移動的路徑,並定位刺繡點的座標。本研究成果尚可擴展至圖案與中文刺繡的探討。
    在規劃刺繡路徑方面,本研究提出了先求最長路徑,再以深度優先搜尋法巡視分枝的演算法。規劃所得之結果接近最佳路徑,甚至擴展至中文字亦能求得可行之路徑。
    在計算刺繡點方面,先以逆傳遞類神經網路辨識中心軸之分枝形態,針對不同的分枝形態,加以適當地分割或合併,並指定中心軸對應之繡線方向。再以polyline和circular arc逼近中心軸,最後配合指定之密度與先前決定之繡線方向,求出刺繡點座標。經由刺繡點座標,即可模擬繡件成品。

    In this thesis, a prototype of an automatic embroidery digitizing system is proposed. First, medial axes are found to represent the embroidery in a compact form. Back-propagation neural network is then used to classify different types of junctions and endpoints along the skeleton. A silk threading plan is then determined via a greedy path planning algorithm that is very efficient in minimizing the number of extraneous path while maintaining the look and feel of an acceptable end product. Experiments have shown that the proposed method can produce satisfactory results on printed English alphabets and on digits 0 through 9.

    圖目錄 Ⅲ 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 問題闡述 3 1.4 研究範圍與限制 11 1.5 論文組織架構 11 第二章 名詞釋義及相關研究 12 2.1 相關研究探討 12 2.2 名詞釋義 14 第三章 特徵擷取 17 3.1 改良式ZHANG演算法 17 3.2 分枝點偵測 20 3.3 端點偵測 21 3.4 實驗結果 21 第四章 路徑規劃 24 4.1 最長路徑 28 4.2 分枝處理 30 4.3 實驗結果 35 第五章 刺繡點定位 43 5.1 中心軸上分枝的處理 43 5.1.1 中心軸分枝形態的種類 43 5.1.2 以逆傳遞網路為分辨器 47 5.2 曲線逼近 49 5.2.1 曲線逼近之模型與演算法 50 5.2.2 中心軸之延展 51 5.3 刺繡點定位 53 5.3.1 中心軸為直線 53 5.3.2 中心軸為弧線 55 5.3.3 改善控制點的連續性 57 5.4 實驗結果 60 5.4.1 中心軸分枝之辨識結果 60 5.4.2 整體實驗結果 61 第六章 結論與未來展望 66 6.1 結論 66 6.2 未來發展方向 67 附錄A 69 A.1 FUZZY C-MEANS ALGORITHM 69 參考文獻 74

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