【作者】 耿杰；

【导师】 刘洲峰； 徐国雄；

【作者基本信息】 中原工学院 ， 信号与信息处理， 2011， 硕士

【摘要】 棉花中异性纤维的存在会严重影响棉纺织品的质量,从而影响棉纺织企业的经济效益。人工挑拣棉花异纤效率慢,漏检及错检率高。随着计算机视觉技术的发展,自动检测异性纤维技术得到了广泛的重视。本文对基于图像处理技术的异纤检测进行了研究,提出了一种基于遗传算法和BP神经网络的异纤识别算法。首先,利用采用直方图均衡、中值滤波方法对异纤图像进行预处理,消除噪声影响。在分析各种分割算法的基础上,采用形态学方法对异纤图像进行分割。然后,分析异纤图像的特点,选择具有类内距较小,类间距较大的特征。本文采用灰度共生矩阵方法计算图像共生矩阵熵、能量、惯性矩、相关4个纹理参数,分别计算各个参数的均值和方差,得到一幅图像的8个纹理特征;并选定六尺度、四方向的Gabor波滤波器组对图像进行滤波,得到24个图像,分别计算它们的均值和均方差,得到一幅图像的48维Gabor特征,并根据不同图像同类特征的方差,将48维降为36维。最后,本文提出一种基于遗传算法的改进BP神经网络方法,对异纤图像进行识别分类。首先根据异纤图像特征的个数确定BP神经网络的输入层神经元个数;根据识别异纤的种类,确定3个输出层神经元,可识别8种类型的异纤。选定神经网络结构后,对随机给定的权值和阈值进行顺序编码,由神经网络的训练误差得出遗传算法的适应度函数,遗传算法对初始权值和阈值进行优化,得到优化后的BP神经网络。利用测试样本对BP神经网络进行训练,然后根据训练后的BP神经网络对测试样本进行识别。实验结果表明,提出算法在异纤检测、识别上取得了较好的结果,具有较好的应用前景。更多还原

【Abstract】 The profile fiber existing in cotton affects the quality of textiles, which will reduce the economic benefits of cotton textile enterprises. For conventional manual picking method, the efficiency is very low, and has the high probability of missed and wrong detection. With the development of computer vision, the technology of automatic detection has drawn widely attention. In this thesis, we research on the detection method of profile fiber based on image processing technology, and propose a detection method based on genetic algorithm and BP neural network.Firstly, we apply histogram equalization and media filtering method into the fiber image to reduce the effect of noise. The effects of several segmentation methods are analyzed, and we select the morphological method as the segmentation algorithm in this thesis.Secondly, we choose the feature with the small distance within the class and the larger distance between the class based on analyzing the character of fiber images. In this thesis, we adopt the gray-level co-occurrence matrix to depict the texture of fiber image because of this capability of blending spatial interaction with gray-level distribution. For the co-occurrence matrix, we calculate its entropy, energy, moment of inertia and relevant, and get a feature vector with 8-dimensions by calculating mean and variance of the four parameters. For the second feature, we select Gabor wavelet filter sets with six scales and four directions to filter the image to get 24 images, and get the feature with 48-dimensions by calculating the mean and variance of the 24 Gabor images. According to the calculating the variance of the same feature between different images, we reduce the 48-dimensions features into 36-dimensions.In the end, we propose an improved BP neural network based on genetic algorithm, and adopt the method to classify the different kinds of the profile fiber. Firstly, we determine the number of neurons of BP neural network at input layer based on the dimension numbers of the feature. Then, based on the number of the kinds of profile fibers, we get the number of neurons at output layer. After selecting the neural network structure, we code the random weights and thresholds. And the improved BP neural network is achieved by optimizing the initial weights and thresholds using generetic algorithm. We train the improved neural network using training sets, and classify the test sets based on the trained neural network.Experimental results show that the proposed method can efficiently detect and classify the profile fiber. And the proposed method has a good prospect.更多还原