基于纹理特征和SVM分类器的铝铸件类型识别

随着全球经济的增长和铝铸件的广泛使用,全球铝铸件消费量逐年上升.由于应用场合不同,导致有各种各样的铝铸件,它们有不同的形状、结构、颜色、质地等.图像的纹理分类作为图像处理应用中的一个重要方面,本文通过分析铝铸件的特点,分别采用灰度共生矩阵、Gabor小波变换提取图像纹理特征,并加以融合对比,使用支持向量机SVM分类算法对特征进行分类.通过实验可知,使用Gabor小波变换对铝铸件分类的识别准确率和识别时间上效果都是最好的.

Recognition of Aluminum Casting Based on Texture Feature and SVM Classifier

With the growth of the global economy and the widespread use of aluminum profiles, the global consumption of aluminum castings has been increasing year by year. Due to the different applications, there are a variety of aluminum castings, they have different shapes, structures, colors, textures and so on. As an important aspect of the image processing application, this study analyzes the features of aluminum castings, extracts the texture features of the image by using the gray level co-occurrence matrix and Gabor wavelet transform, respectively, and compares them with the SVM classification algorithm of SVM feature classification, test recognition accuracy, experimental results were compared for the classification of aluminum castings obtained Gabor wavelet transform using both the recognition accuracy or recognition of time on the results are the best.