本文選題：布匹瑕疵檢測 + 人眼視覺注意機(jī)制��； 參考：《江蘇大學(xué)》2017年碩士論文

【摘要】：傳統(tǒng)布匹瑕疵檢測方法是依賴于檢測工人肉眼對成品布匹進(jìn)行人工瑕疵檢測,而現(xiàn)存的計算機(jī)視覺布匹瑕疵檢測方法主要基于對無瑕疵布匹圖像提取的特征進(jìn)行學(xué)習(xí),從而進(jìn)行布匹瑕疵檢測。該類方法需要對無瑕疵布匹圖像進(jìn)行對比學(xué)習(xí),且對特定角度下采集的布匹實(shí)驗(yàn)圖像有嚴(yán)格要求。上述方案依賴于無瑕疵布匹圖像的先驗(yàn)知識學(xué)習(xí),對不同尺度的布匹或多種布匹同時檢測缺乏考慮,無法同時適用于不同尺度的布匹檢測,從而導(dǎo)致誤檢,魯棒性低。本論文針對上述問題提出了基于Gabor和視覺信息的布匹瑕疵檢測方法。該方案構(gòu)建了布匹瑕疵數(shù)據(jù)庫,根據(jù)人眼能夠快速準(zhǔn)確的從不同尺度與不同紋理布匹圖像中鑒別瑕疵區(qū)域的現(xiàn)象,將人眼視覺引入布匹瑕疵檢測中,從而提出了改進(jìn)的多通道Gabor布匹瑕疵檢測方法與基于改進(jìn)視覺顯著性的布匹瑕疵檢測方法。本文的主要研究內(nèi)容如下:⑴針對現(xiàn)階段布匹檢測方法依賴于無瑕疵布匹圖像的先驗(yàn)知識學(xué)習(xí),無法同時對不同尺度的紡織工廠線采集到的布匹圖像進(jìn)行有效的瑕疵檢測的問題,提出一種基于改進(jìn)多通道Gabor的布匹瑕疵檢測方法。該方法采用改進(jìn)多通道Gabor濾波器對布匹圖像進(jìn)行濾波,通過子塊計分方法對多通道Gabor濾波結(jié)果進(jìn)行選擇,將選擇出的多通道進(jìn)行融合,通過閾值分割,得到瑕疵區(qū)域。實(shí)驗(yàn)結(jié)果表明該方法與傳統(tǒng)的布匹瑕疵檢測方法形態(tài)學(xué)方法、MRF方法比較,可以有效提高對典型的布匹瑕疵檢測的準(zhǔn)確率。⑵針對改進(jìn)多通道Gabor的布匹瑕疵檢測檢測效率低的問題,提出一種改進(jìn)視覺顯著性的布匹瑕疵檢測方法。本文所述方法在經(jīng)典的視覺顯著性模型基礎(chǔ)上得到布匹圖像的自底向上的顯著性特征,并提出一種自頂向下的熵、能量顯著性特征計算方法。該方法通過對顯著性特征圖進(jìn)行融合,使用最大類間方差法進(jìn)行分割顯著性特征圖,得到布匹圖像視覺顯著性區(qū)域。實(shí)驗(yàn)結(jié)果表明該方法與⑴中比較,在保證識別率的基礎(chǔ)上,能夠快速檢測典型布匹瑕疵。
[Abstract]:The traditional method of fabric defect detection depends on the manual flaw detection of finished fabric with the naked eyes of workers, while the existing computer vision fabric defect detection method is mainly based on the features of the image extraction of unblemished fabric. Thus the fabric defect detection is carried out. This kind of method needs to compare and study the image of the blemeless cloth, and has strict requirements for the experimental images of cloth collected at a specific angle. The above scheme relies on the prior knowledge learning of the imperfections of fabric images, and it lacks consideration of the simultaneous detection of different sizes of cloth or fabrics, and can not be applied to fabric detection of different scales at the same time, which leads to false detection and low robustness. In this paper, a cloth defect detection method based on Gabor and visual information is proposed. According to the phenomenon that the human eyes can quickly and accurately distinguish the defective areas from different scales and different textures of cloth images, the human visual system is introduced into fabric defect detection. An improved multi-channel Gabor fabric defect detection method and a fabric defect detection method based on improved visual significance are proposed. The main research contents of this paper are as follows: 1. Aiming at the present fabric detection method, which depends on the prior knowledge learning of the image of the flawless cloth, The defect detection method based on improved multi-channel Gabor is proposed, which can not detect the defects of fabric images collected from textile factory lines of different scales at the same time. The improved multi-channel Gabor filter is used to filter the fabric image, the sub-block scoring method is used to select the multi-channel Gabor filtering results, the selected multi-channel is fused, and the defect region is obtained by threshold segmentation. The experimental results show that this method is compared with the traditional cloth defect detection method, morphology method and MRF method. It can effectively improve the accuracy of the typical fabric defect detection. 2 in order to improve the efficiency of fabric defect detection based on multi-channel Gabor, an improved visual significant fabric defect detection method is proposed. Based on the classical visual saliency model, the bottom-up salience features of cloth images are obtained, and a top-down entropy and energy salience feature calculation method is proposed. In this method, the significant feature map is fused and the significant feature map is segmented by using the maximum inter-class variance method, and the visual significance region of the cloth image is obtained. The experimental results show that the proposed method can quickly detect typical fabric defects on the basis of guaranteed recognition rate.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS101.97;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 董蓉;李勃;;基于最優(yōu)參數(shù)非線性GLCM的織物瑕疵檢測算法[J];計算機(jī)工程與設(shè)計;2015年09期

2 王春瑤;陳俊周;李煒;;超像素分割算法研究綜述[J];計算機(jī)應(yīng)用研究;2014年01期

3 曾志宏;李建洋;鄭漢垣;;融合深度信息的視覺注意計算模型[J];計算機(jī)工程;2010年20期

4 李旭超;朱善安;;圖像分割中的馬爾可夫隨機(jī)場方法綜述[J];中國圖象圖形學(xué)報;2007年05期

5 陶亮,莊鎮(zhèn)泉;復(fù)雜背景下人眼自動定位[J];計算機(jī)輔助設(shè)計與圖形學(xué)學(xué)報;2003年01期

6 壽天德，周逸峰;視覺系統(tǒng)皮層下細(xì)胞的方位和方向敏感性[J];生理學(xué)報;1996年02期

相關(guān)博士學(xué)位論文 前3條

1 崔玲玲;布匹瑕疵識別中的關(guān)鍵技術(shù)研究[D];西安電子科技大學(xué);2014年

2 魏昱;圖像顯著性區(qū)域檢測方法及應(yīng)用研究[D];山東大學(xué);2012年

3 鄒超;布匹疵點(diǎn)在線檢測系統(tǒng)研究[D];華中科技大學(xué);2009年

相關(guān)碩士學(xué)位論文 前1條

1 成培瑞;基于多尺度區(qū)域?qū)Ρ鹊囊曈X顯著性檢測算法研究[D];中國科學(xué)院研究生院(長春光學(xué)精密機(jī)械與物理研究所);2015年

，

本文編號：2088873