發(fā)布時間：2018-03-26 14:32

  本文選題：疵點檢測　切入點：Gabor濾波簇　出處：《武漢紡織大學(xué)》2017年碩士論文

【摘要】：提高疵點辨識精度和效率對提升紡織品質(zhì)量具有重要意義。針對疵點圖像光照不均和對比度低的問題,開展基于Gabor小波簇的疵點圖像增強方法研究。首先,利用由3個尺度和5個方向的15個Gabor濾波簇對疵點圖片進行不同方向和尺度的濾波,減少圖像不均和對比度低對特征提取精度的影響;然后,將濾波圖像劃分成面積相等互不重合的鄰域,并從鄰域中提取高維特征向量。接下來,針對Gabor特征向量維數(shù)高和冗余信息大的問題,使用等距映射方法對Gabor特征進行非線性降維,剔除高維特征中冗余信息,強化分類器擬合能力,達到強化Gabor特征靈敏度的目的。其次,針對等距映射算法在Gabor特征降維過程中遇到的結(jié)構(gòu)參數(shù)選擇困難的問題,應(yīng)用Ncut準(zhǔn)則作為適度函數(shù)建立結(jié)構(gòu)參數(shù)優(yōu)化模型,使用離散離子群算法進行參數(shù)優(yōu)化,提出基于粒子群和Ncut準(zhǔn)則的等距映射參數(shù)優(yōu)化方法;針對等距映射算法新增樣本低維特征提取困難的問題,利用樣本在高維空間和低維空間幾何結(jié)構(gòu)相同的假設(shè)建立新樣本低維嵌入模型,提出新增樣本低維特征提取方法。最后,將低維特征輸入概率神經(jīng)網(wǎng)絡(luò)分類器中進行疵點辨識,突破疵點圖像光照不均和對比度低等對疵點檢測精確的制約。實驗研究中,利用2組不同紋理的疵點圖片數(shù)據(jù)進行實驗研究,結(jié)果表明:基于Gabor濾波簇和等距映射算法的疵點檢測準(zhǔn)確率達97%左右。但是,同時也存在濾波器數(shù)量多、運算量大的問題。為提高疵點檢測效率,利用頻域協(xié)調(diào)算法抗噪能力強和計算量小優(yōu)點,替代Gabor濾波器簇用于疵點圖像增強,達到提高檢測效率的目的。針對頻域協(xié)調(diào)算法在疵點檢測中遇到的疵點辨識精度受高斯濾波器模板尺寸影響大的問題,利用Ncut準(zhǔn)則作為適度函數(shù),建立高斯濾波器模板尺寸優(yōu)化模型,使用離散離子群算法進行參數(shù)優(yōu)化;針對Lab顏色空間對單一顏色紡織品疵點顯著效果不明顯的問題,利用HSV顏色空間代替Lab顏色空間,強化顯著效果;針對色調(diào)特征、飽和度特征和亮度特征取值范圍不同且變化不一致導(dǎo)致顯著值不能很好地體現(xiàn)各個分量作用的問題,展開了色調(diào)特征、飽和度特征和亮度特征的歸一化研究,建立顯著值歸一化模型。最后,采用灰度共生矩陣進行特征提取,將提取的特征向量輸入概率神經(jīng)網(wǎng)絡(luò)進行疵點辨識。通過改進頻域協(xié)調(diào)顯著方法和Gabor濾波簇方法的對比實驗研究發(fā)現(xiàn):基于改進頻域協(xié)調(diào)顯著算法的疵點檢測方法能夠在保證疵點檢測精度的前提下,運算速度比Gabor小波方法提高70%。
[Abstract]:Improving the accuracy and efficiency of defect identification is of great significance to improve the quality of textiles. Aiming at the problem of uneven illumination and low contrast of defect image, the defect image enhancement method based on Gabor wavelet cluster is studied. Using 15 Gabor filter clusters with three scales and five directions to filter defect images in different directions and scales to reduce the influence of uneven image and low contrast on the accuracy of feature extraction. The filtered image is divided into two neighborhoods whose area is equal to each other, and high dimensional feature vectors are extracted from the neighborhood. Then, for the problems of high dimension of Gabor eigenvector and large redundant information, The method of equidistant mapping is used to reduce the nonlinear dimension of Gabor features, eliminate redundant information from high dimensional features, and enhance the classifier fitting ability to enhance the sensitivity of Gabor features. Aiming at the difficulty of selecting structural parameters in the process of Gabor feature dimensionality reduction using the isometric mapping algorithm, the structural parameter optimization model is established by using the Ncut criterion as an appropriate function, and the discrete ion swarm algorithm is used to optimize the structure parameters. A parameter optimization method for equidistant mapping based on particle swarm optimization and Ncut criterion is proposed, and it is difficult to extract low-dimensional feature of new samples in offset mapping algorithm. Based on the assumption that the geometric structure of samples is the same in high-dimensional space and low-dimensional space, a new low-dimensional embedding model of samples is established, and a new low-dimensional feature extraction method is proposed. Finally, the low-dimensional feature is input into the probabilistic neural network classifier for defect identification. In the experimental research, two groups of defect image data of different textures are used to carry out experimental research. The results show that the defect detection accuracy is about 97% based on Gabor filter cluster and equidistant mapping algorithm. Using the advantages of strong anti-noise ability and small computational complexity of frequency domain coordination algorithm, instead of Gabor filter cluster, it can be used for defect image enhancement. Aiming at the problem that the defect identification accuracy of frequency domain coordination algorithm is greatly affected by the size of Gao Si filter template, the Ncut criterion is used as a moderate function. The template size optimization model of Gao Si filter is established, and the discrete ion swarm algorithm is used to optimize the parameters. Aiming at the problem that the Lab color space has no obvious effect on single color textile defects, the HSV color space is used to replace the Lab color space. Aiming at the problem that the significant value of each component is not well reflected due to the difference in the range of the values of hue feature, saturation feature and luminance feature, the color feature is developed. The normalization of saturation feature and luminance feature is studied, and the normalized model of significant value is established. Finally, the gray level co-occurrence matrix is used to extract the feature. The feature vector input probabilistic neural network is used for defect identification. Through comparing the improved frequency domain coordination saliency method and Gabor filter cluster method, it is found that the defect detection method based on improved frequency domain coordination saliency algorithm is based on improved frequency domain coordination saliency algorithm. The method can guarantee the precision of defect detection, The operation speed is 70% higher than that of Gabor wavelet method.
【學(xué)位授予單位】：武漢紡織大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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