基于图像分割的纺织品纹样提取算法研究

Research on textile pattern extraction algorithm based on image segmentation

图像分割作为图像处理技术的关键,在纺织品纹样提取中发挥至关重要的作用。为了有效帮助相关学者根据纺织品图像特征快速选择合适的分割方法,首先介绍了分割预处理算法,列举纺织品图像的噪声、纹理平滑方法;其次梳理总结不同类型的分割算法,对分割常用算法及其优缺点进行总结,同时列举了部分优化分割算法的应用;最后围绕融合分割算法在纺织品纹样提取中的应用,对纺织品纹样提取的分割算法优化思路及融合方法原理作出总结。研究认为,对传统算法加以改进或是采用多算法融合的方式,可以有效提高纺织品纹样提取的精度和效率。

Image segmentation,as the key to image processing technology,plays a vital role in textile pattern extraction.In order to effectively help relevant scholars quickly select appropriate segmentation methods according to the characteristics of textile images,the segmentation preprocessing algorithms are enumerated,including noise and texture smoothing methods for textile images.Secondly,the different types of segmentation algorithms are sorted out and summarized,the common segmentation algorithms and their advantages and disadvantages are summarized,and the applications of some optimization segmentation algorithms are listed.Finally,focusing on the application of fusion segmentation algorithm in textile pattern extraction,the optimization ideas of segmentation algorithm and the principle of fusion method for textile pattern extraction are summarized.It is believed that the improvement of traditional algorithms or the fusion of multiple algorithms can effectively improve the accuracy and efficiency of textile pattern extraction.