本文選題：織物圖像 + 疵點檢測��； 參考：《中原工學(xué)院》2017年碩士論文

【摘要】：織物疵點檢測是紡織品質(zhì)量控制系統(tǒng)中一個核心環(huán)節(jié),直接影響著系統(tǒng)的性能。從紋理復(fù)雜的織物圖像中檢測形態(tài)多樣的疵點具有重要的應(yīng)用價值。該問題的解決也有利于對其它工業(yè)產(chǎn)品表面缺陷檢測提供新的解決思路�，F(xiàn)有織物疵點檢測多采用傳統(tǒng)模式識別的方法,如統(tǒng)計分析、頻譜分析等。近年來,受壓縮感知和稀疏表示理論的推動,低秩稀疏矩陣分解模型在圖像處理和模式識別中也獲得廣泛的應(yīng)用,并且在目標(biāo)檢測中達(dá)到很好地檢測效果。低秩稀疏矩陣分解模型與人類視覺系統(tǒng)的低秩稀疏性相吻合,通過將圖像矩陣分解為低秩陣和稀疏陣,實現(xiàn)目標(biāo)與背景的有效分離,特別地,對于織物圖像,視覺上具有高度冗余性,相對于自然圖像中的目標(biāo)檢測,織物疵點檢測能夠更好地符合了低秩稀疏矩陣分解模型。另外,織物圖像的特征提取也是疵點檢測的關(guān)鍵步驟。對圖像提取好的特征,并構(gòu)建合適的低秩稀疏矩陣分解模型,并利用有效的優(yōu)化求解方法和對分解得到的疵點分布圖采用有效的閾值分割算法,才能準(zhǔn)確和有效的定位出疵點的位置和區(qū)域。為此,本論文提出應(yīng)用方向梯度直方圖和低秩分解、基于Gabor濾波器和低秩分解、基于GHOG和低秩矩陣恢復(fù)以及基于生物視覺特征提取及低秩表示的織物疵點檢測算法。所做的工作以及研究成果如下:1)提出基于Gabor濾波器和低秩分解的織物疵點檢測算法。首先,對織物圖像提取Gabor濾波器特征,再對生成的特征圖進(jìn)行均勻分塊,并將所有的圖像塊特征組合成特征矩陣。對于生成的特征矩陣,構(gòu)建合適的低秩分解模型,通過快速近端梯度方法優(yōu)化求解,從而生成低秩矩陣和稀疏矩陣,最后采用最優(yōu)閾值分割算法對由稀疏陣生成的疵點分布圖進(jìn)行分割,從而定位出疵點的區(qū)域和位置。2)提出了應(yīng)用方向梯度直方圖(HOG)和低秩分解的織物疵點檢測算法。首先,將織物圖像劃分為大小相同的圖像塊,提取每個圖像塊的HOG特征,并將圖像塊特征組成特征矩陣。針對特征矩陣,構(gòu)建有效的低秩分解模型,通過增廣拉格朗日方法優(yōu)化求解,生成低秩陣和稀疏陣;最后采用最優(yōu)閾值分割算法對由稀疏陣生成的疵點分布圖進(jìn)行分割,從而定位出疵點區(qū)域。3)提出了基于GHOG及低秩分解的模式織物疵點檢測算法。對于前兩種檢測算法只能檢測紋理較為簡單的織物疵點圖像,本論文提出了一種基于GHOG和低秩恢復(fù)的模式織物疵點檢測算法。首先,對圖像進(jìn)行Gabor濾波,從而生成相應(yīng)的Gabor特征圖,然后將對應(yīng)的方向上的Gabor特征圖進(jìn)行均勻分塊,并提取HOG特征,從而生成最后的GHOG特征,并將所有圖像塊的特征向量進(jìn)行級聯(lián)生成特征矩陣。對特征矩陣,構(gòu)建低秩分解模型,并利用方向交替方法進(jìn)行優(yōu)化求解,產(chǎn)生低秩矩陣和稀疏矩陣,并對由稀疏矩陣產(chǎn)生的疵點分布圖采用最優(yōu)閾值分割算法進(jìn)行分割,從而定位出疵點的位置。4)提出了基于生物視覺特征提取及低秩表示的織物疵點檢測算法。生物視覺對客觀世界的表征是完備的,能支持各種復(fù)雜的高級視覺任務(wù)。本文引入一種借鑒人類視覺感知和視網(wǎng)膜表征機理的特征表示方法。在該特征表示的基礎(chǔ)上,利用KSVD在測試圖像上訓(xùn)練出正�？椢飯D像塊字典�；趯W(xué)習(xí)出的字典,建立特征矩陣的低秩表示模型,并利用ADMM方法進(jìn)行求解,從而提高算法檢測效果及自適應(yīng)性。
[Abstract]:Fabric defect detection is a key link in the quality control system of textiles, which directly affects the performance of the system. It is of great application value to detect the variety of defects from the texture of a complex texture. The solution of this problem is also helpful to provide a new solution for the surface defect detection of other industrial products. Point detection mostly uses traditional pattern recognition methods, such as statistical analysis, spectrum analysis and so on. In recent years, the low rank sparse matrix decomposition model has also been widely used in image processing and pattern recognition, and has been widely used in image processing and pattern recognition. The model is consistent with the low rank sparsity of human visual system. By decomposing the image matrix into low rank array and sparse array, the target and the background are separated effectively. In particular, the fabric image is highly redundant. The fabric defect detection can better meet the low rank sparsity compared with the target detection in the natural image. In addition, the feature extraction of the image of the fabric is also the key step of the defect detection. The features extracted from the image and the suitable low rank sparse matrix decomposition model are constructed, and the effective optimization method and the effective threshold segmentation algorithm are used to determine the defect distribution. In this paper, we propose the application of directional gradient histogram and low rank decomposition, based on Gabor filter and low rank decomposition, GHOG and low rank matrix restoration, and fabric defect detection algorithm based on biological visual feature extraction and low rank representation. The work and research results are as follows: 1) proposed based on Gab Or filter and low rank decomposition algorithm for fabric defects detection. First, the feature of the fabric image is extracted from the Gabor filter, and then the generated feature graph is partitioned evenly, and all the image block features are combined into the feature matrix. In the end, the low rank matrix and the sparse matrix are generated. Finally, the optimal threshold segmentation algorithm is used to segment the defect distribution map generated by the sparse array, and the defect location and location.2 are located. The fabric defect detection algorithm is proposed by using the direction gradient histogram (HOG) and the low rank decomposition. First, the fabric image is divided into the size phase. In the same image block, the HOG features of each image block are extracted and the feature matrix of the image block is formed. An effective low rank decomposition model is constructed for the feature matrix. The low rank array and sparse array are generated by the augmented Lagrange method to generate the low rank array and the sparse array. Finally, the optimal threshold segmentation algorithm is used to divide the defect distribution map generated by the sparse array. The defect detection algorithm based on GHOG and low rank decomposition is proposed. The first two detection algorithms can only detect the fabric defect image with simple texture. In this paper, a pattern detection algorithm based on GHOG and low rank recovery is proposed in this paper. First, the image is filtered by Gabor, The corresponding Gabor feature graph is generated, then the Gabor feature map of the corresponding direction is partitioned evenly, and the features of the HOG are extracted, thus the final GHOG features are generated, and the feature vectors of all the image blocks are cascaded to generate the feature matrix. The low rank decomposition model is constructed for the feature matrix, and the direction alternation method is used to optimize the feature matrix. A low rank matrix and a sparse matrix are generated, and the defect distribution map produced by the sparse matrix is segmented with the optimal threshold segmentation algorithm, and the location of the defect location.4 is located. A fabric defect detection algorithm based on the feature extraction of biological vision and low rank representation is proposed. The representation of the raw object vision to the objective world is complete and can be supported. In this paper, we introduce a feature representation method that draws on the mechanism of human visual perception and retina representation. On the basis of this feature, we use KSVD to train normal fabric image block dictionary on the test image. Based on the learning dictionary, the low rank representation model of the feature matrix is built, and ADMM is used. Method is used to improve the detection effect and adaptability of the algorithm.

【學(xué)位授予單位】：中原工學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS101.97;TP391.41

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