织物纹样特征提取与匹配方法比较

Comparison of feature extracting and matching methods for fabric patterns

针对织物纹样自动识别过程中因尺度、旋转和褶皱等因素引起图像差异的问题,探索了复杂纹样特征的准确提取与匹配方法。以江崖海水纹样为例,采集尺度、旋转、模糊、光照、褶皱5种变化下的织物纹样图像,分别运用尺度不变特征变换(SIFT)、快速鲁棒性尺度不变特征(SURF)、二进制鲁棒不变可扩展关键点(BRISK)3种方法提取纹样局部特征,然后采用欧氏距离进行特征匹配计算,最后通过随机抽样一致算法剔除误匹配对。结果表明:采用BRISK算法的准确配对率最高,平均准确匹配率达87.10%;褶皱对织物特征匹配的影响最大,该变化下BRISK算法的鲁棒性优于SIFT和SURF算法;BRISK算法速度最快,图像平均匹配时间0.551 s;在织物纹样特征匹配中,BRISK算法比SIFT和SURF算法具有更好的适用性。

In fabric pattern recognition,variations in image scaling,rotating,folding and other deformation in the sampling process cause errors,and this paper investigated the improvement of the exacting and matching method for fabric patterns.To explore the applicability of feature extracting and matching methods for complicated patterns,the river cliff water pattern was taken as experimental samples.5 types of images of the fabric pattern feature were acquired under scaling,rotation,fuzzy,illumination and drape respectively,and fabric pattern features were extracted using scale-invariant feature transform(SIFT),speeded-up robust features(SURF),binary robust invariant scalable key-points(BRISK)algorithms.Euclidean distance method was adopted for matching calculation,eliminating false match points by random sample consensus algorithm.The results show that BRISK algorithm is the best in matching ratio,which is 87.10%on average.Folding is proven to have the greatest impacts on fabric feature matching,and the robustness of BRISK algorithm under folding change is better than SIFT and SURF algorithms.BRISK algorithmic speed is the fastest,taking an average time of 0.551 s to perform the image feature extraction and matching.In fabric pattern matching,BRISK algorithm is demonstrated better applicability than SIFT or SURF algorithms.