【摘要】：機(jī)織物圖像組織結(jié)構(gòu)識(shí)別的研究,在機(jī)織物組織結(jié)構(gòu)識(shí)別方面具有重要的應(yīng)用價(jià)值,同時(shí)在紋理分析與識(shí)別算法研究方面具有很好的理論意義。本文在機(jī)織物圖像組織結(jié)構(gòu)識(shí)別相關(guān)知識(shí)基礎(chǔ)上,對(duì)紋理分析與識(shí)別方法進(jìn)行研究,改進(jìn)了機(jī)織物組織結(jié)構(gòu)識(shí)別的流程和框架,提出了基于紗線邊界特征的組織結(jié)構(gòu)分類和基于S-Gabor特征和分類矯正的組織圖識(shí)別兩步機(jī)織物識(shí)別策略,在機(jī)織物圖像組織結(jié)構(gòu)識(shí)別過程中將兩個(gè)不同特征分層次融合使用。組織點(diǎn)紗線邊界特征計(jì)算過程中,采用亮度變化信息確定組織點(diǎn)紗線邊界信息,首先進(jìn)行局部組織點(diǎn)歸一化處理,進(jìn)一步定義“相鄰組織點(diǎn)圖像間亮度絕對(duì)變化”以及“相鄰組織點(diǎn)圖像間亮度相對(duì)變化”兩步操作實(shí)現(xiàn)組織點(diǎn)紗線邊界特征提取。在組織循環(huán)紗線數(shù)計(jì)算操作中,利用平均海明距離表示兩條紗線間的相似度,并在利用極小值點(diǎn)得出組織循環(huán)紗線數(shù)后,以不同長(zhǎng)度分割所得相鄰等長(zhǎng)子序列平均相關(guān)系數(shù)進(jìn)行修正。組織結(jié)構(gòu)分類操作中,提出同時(shí)利用機(jī)織物組織循環(huán)紗線數(shù)和預(yù)識(shí)別組織圖斜向相關(guān)性對(duì)機(jī)織物組織結(jié)構(gòu)進(jìn)行分類的方案。組織圖識(shí)別過程中,利用Steerable Filter和Gabor推導(dǎo)得出S-Gabor變換,討論了S-Gabor變換的性質(zhì),并給出了S-Gabor變換生物學(xué)原理,在理論上驗(yàn)證S-Gabor變換對(duì)圖像梯度信息具有更好的特征提取效果。進(jìn)一步使用S-Gabor變換提取非斜紋組織點(diǎn)特征,并進(jìn)行PCA降維,而后利用SVM分類組織點(diǎn)屬性,最后采用不同組織點(diǎn)誤檢方案分類矯正平紋組織、斜紋及其變化組織、緞紋組織預(yù)識(shí)別組織圖。本文基于天津工業(yè)大學(xué)機(jī)織物圖片數(shù)據(jù)庫(kù)進(jìn)行實(shí)驗(yàn),該數(shù)據(jù)庫(kù)樣本組織結(jié)構(gòu)種類全面、圖像信息多變、干擾因素較多,研究?jī)r(jià)值和參考價(jià)值較高,實(shí)驗(yàn)證明本文提出S-Gabor變換能很好地提取相關(guān)紋理特征;本文組織循環(huán)紗線數(shù)計(jì)算方法、組織結(jié)構(gòu)分類方法和組織圖識(shí)別方法在數(shù)據(jù)庫(kù)中具有極高的準(zhǔn)確率,分別為99.25%、99.62%、96.98%,魯棒性較高;本文組織圖識(shí)別方法很好地平衡了基于紗線邊界特征組織結(jié)構(gòu)識(shí)別的高效率和基于S-Gabor特征組織結(jié)構(gòu)識(shí)別的高準(zhǔn)確率。
[Abstract]:The research of woven fabric image tissue structure recognition has important application value in woven fabric tissue structure recognition, and has good theoretical significance in texture analysis and recognition algorithm research. In this paper, based on the knowledge of woven fabric image organizational structure recognition, the texture analysis and recognition methods are studied, the flow and framework of woven fabric tissue structure recognition are improved, and the organization structure classification based on yarn boundary features and the recognition strategy of two-step woven fabric based on S-Gabor features and classification correction are proposed. In the process of tissue structure recognition of woven fabric image, two different features are used in hierarchical fusion. In the process of calculating the boundary characteristics of tissue point yarn, the yarn boundary information of organization point is determined by using brightness change information. Firstly, the local organization point is normalized, and the two steps of "absolute change of brightness between adjacent organization point images" and "relative change of brightness between adjacent organization point images" are further defined to realize the extraction of yarn boundary feature of organization point. In the calculation operation of the number of tissue circulating yarns, the average hamming distance is used to represent the similarity between the two yarns, and after the number of tissue circulating yarns is obtained by using the minimum point, the average correlation coefficient of adjacent isometric subsequences obtained by different length segmentation is modified. In the classification operation of fabric structure, a scheme is proposed to classify the microstructure of woven fabric by using the number of recycled yarns in woven fabric and the oblique correlation of pre-recognized tissue diagram at the same time. In the process of organization diagram recognition, the S-Gabor transform is deduced by Steerable Filter and Gabor, the properties of S-Gabor transform are discussed, and the biological principle of S-Gabor transform is given. It is theoretically verified that S-Gabor transform has better feature extraction effect on image gradient information. Furthermore, S-Gabor transform is used to extract the characteristics of non-twill tissue points, and then PCA is used to reduce the dimension of non-twill tissue points, and then SVM classification is used to organize point attributes. Finally, different tissue point misdetection schemes are used to correct plain tissue, twill and its changing tissue, and satin tissue pre-recognizes tissue diagram. In this paper, the experiment is carried out based on the woven fabric picture database of Tianjin University of Technology. The sample structure of the database is comprehensive, the image information is changeable, there are many interference factors, and the research value and reference value are high. The experiment proves that the S-Gabor transform can extract the relevant texture features very well. In this paper, the calculation method of organization circulation yarn number, the classification method of organization structure and the recognition method of organization chart have very high accuracy in the database, which are 99.25%, 99.62% and 96.98%, respectively, and the robustness is high. The organization chart recognition method in this paper balances the high efficiency based on yarn boundary feature organization structure recognition and the high accuracy rate based on S-Gabor feature organization structure recognition.
【學(xué)位授予單位】：河北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TS101.923;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張江豐;樊臻;張森林;;基于核模糊聚類的機(jī)織物組織自動(dòng)識(shí)別[J];紡織學(xué)報(bào);2013年12期

2 包曉敏;曹作寶;汪亞明;周硯江;朱寒宇;;基于嗅覺神經(jīng)網(wǎng)絡(luò)的織物組織識(shí)別[J];紡織學(xué)報(bào);2011年04期

3 辛斌杰;余序芬;吳兆平;;應(yīng)用圖像分析技術(shù)自動(dòng)識(shí)別織物的組織結(jié)構(gòu)[J];東華大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年01期

4 潘如如;高衛(wèi)東;劉基宏;王鴻博;;機(jī)織物組織自動(dòng)識(shí)別技術(shù)[J];紡織學(xué)報(bào);2010年06期

5 任海軍;孫瑞志;宋強(qiáng);;基于SMO算法的織物組織結(jié)構(gòu)識(shí)別[J];計(jì)算機(jī)工程與設(shè)計(jì);2009年22期

6 謝莉青;于偉東;;色織物組織的灰度特征與自動(dòng)識(shí)別[J];青島大學(xué)學(xué)報(bào)(工程技術(shù)版);2007年03期

7 張一;耿兆豐;;基于基元特征匹配的織物結(jié)構(gòu)分析與識(shí)別[J];微計(jì)算機(jī)信息;2006年01期

8 吳海虹,張明敏,潘志庚,裘文波;彩色圖像的織物組織自動(dòng)識(shí)別[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2005年08期

9 高衛(wèi)東,劉基宏,徐伯俊,狄煒,薛衛(wèi);織物組織結(jié)構(gòu)的自動(dòng)識(shí)別[J];棉紡織技術(shù);2002年04期

10 高衛(wèi)東,劉基宏,徐伯俊,狄煒,薛衛(wèi);織物中經(jīng)紗排列參數(shù)的自動(dòng)識(shí)別[J];棉紡織技術(shù);2002年03期

相關(guān)博士學(xué)位論文 前1條

1 潘如如;基于數(shù)字圖像處理的機(jī)織物結(jié)構(gòu)參數(shù)識(shí)別[D];江南大學(xué);2010年

相關(guān)碩士學(xué)位論文 前4條

1 張江豐;基于圖像處理的機(jī)織物組織自動(dòng)識(shí)別的研究[D];浙江大學(xué);2013年

2 楊開富;基于多視覺特征的非經(jīng)典感受野模型及應(yīng)用研究[D];電子科技大學(xué);2012年

3 涂新星;基于機(jī)器視覺的織物外觀數(shù)字化分析方法及系統(tǒng)設(shè)計(jì)[D];東華大學(xué);2012年

4 李杏圓;基于圖像處理的機(jī)織物參數(shù)分析與組織結(jié)構(gòu)識(shí)別的研究[D];東華大學(xué);2007年

，

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