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題 名 | 類神經網路於辨識織物瑕疵自動化系統之應用=The Application of an Artificial Neural Network in an Automatic Inspection System for Fabric Defects |
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作 者 | 蔡宜壽; 林宗華; 林正中; | 書刊名 | 技術學刊 |
卷 期 | 12:1 1997.03[民86.03] |
頁 次 | 頁171-182 |
分類號 | 448.945 |
關鍵詞 | 類神經網路; 特徵向量; 自動回歸模型; 共發矩陣; Artificial neural network; Feature vector; Autoregressive model; Co-occurrence matrix; |
語 文 | 中文(Chinese) |
中文摘要 | 本文針對類神經網路技術應用於辨識織物瑕疵種類的效率及準確性作評估。首先 本文選出於織造時,經常發生的四種瑕疵,分別利用類神經網路的倒傳遞演算法予以訓練學 習而達到準確辨認之目的。吾人利用兩種參數萃取法求出四個特徵參數,其中以圓形自動回 歸模型法求出�`與��二參數,另再以共發矩陣法求出 CON 及 CON 二參數,然後再以�`,�� ,CON 及 CON 等四參數組成一個特徵向量供類神經網路學習或測試之用。 經實驗結果顯示 以四特徵參數組成的特徵向量之辨識準確率可達 75% 且辨識速度極快, 可於數分之一秒時 間內做出辨識結果,與原先所期待的相當符合且令人滿意。 |
英文摘要 | In this paper, the efficiency and accuracy of discrimination in classi fication of fabric defects by a neural network are evaluated. four kinds of fabric defects that frequently occur during weaving are chosen and learned by a neural network. Based on the principle of a back-propagation learning algorithm, fabric defects can be detected and classified exactly. Four feature parameters obtained from two extraction methods are employed to represent the texture images of fabric defects in this research. Two of them. �` and ��, are obtained from a stochastic parametric method of imaging called a "circularly symmetric autoregressive model." The other two, namely CON and CON, are obtained from the gray level co-occurrence matrix method. Using the feature vectosors consisting of �`, ��, CON and CON, the texture image of each kind of fabric defects can have an adequate and general representation, and the aim of promoting a correct classification rate is fulfilled. |
本系統中英文摘要資訊取自各篇刊載內容。