織物組織在織物分析上扮演著相當重要的角色，近來電腦視覺在織物組織辨識上的方法是利用織物影像中經緯浮點的辨識來決定織物組織，但因檢測時光學環境、織物及紗線的外觀差異性及檢測的電腦視覺方法穩定性仍有待加強，使得目前織物組織辨識的電腦視覺系統的實用性及容錯能力有待提升。所以本研究提出對梭織物的織物組織自動化辨識方法，目的在於提高辨識系統的實用性及容錯性。本研究利用兩階段的倒傳遞類神經網路來建構織物組織辨識的電腦視覺系統的分類器，其特徵值選擇了一階紋理及二階共生矩陣。由實驗結果可知本研究的織物組織辨識方法可正確地進行梭織物的織布物組織的自動化辯識，及驗証所提出之方法在織物分析上之可行性。

Enabling computer vision to recognize texture of different fabrics is an important factor in fabric analysis and a progress in research. Throughout the process, the warp and weft floating points of the fabric’s image determine the fabric’s texture. However, different optics environment, fabric materials, and computer vision method stability can lead to variable results, causing computer vision system’s usability and the fault-tolerant ability to be focused in research. We proposed a new automatic recognition algorithm for fabric weave pattern recognition. We also used neural network to construct the computer vision system to recognize fabric’s texture, and to increase this system’s reliability and fault-tolerance. In this study, we used first- and second-order statistics method and composed the classified system with two-step back-propagation network. Experimental results indicated that fabric patterns can be identified clearly by our proposed method.

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