摘要
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.
作者
汪会
孙洁
丁笑君
龙颖
邹奉元
WANG Hui;SUN Jie;DING Xiaojun;LONG Ying;ZOU Fengyuan(School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China;Zhejiang Provincial Research Center of Clothing Engineering Technology, Zhejiang Sic-Tech University, Hangzhou, Zhejiang 310018, China;Zhejiang Garment Personalized Customization Collaborative Innovation Center, Hangzhou, Zhejiang 310018, China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2020年第4期45-50,共6页
Journal of Textile Research
基金
浙江省2011协同创新中心科技研发专项资助项目(17034005-F)
2019年浙江省大学生科技创新活动计划项目(2019R406070)
浙江理工大学2018年优秀研究生学位论文培育基金项目(2018-XWLWPY-M-04-04)。
关键词
江崖海水纹样
特征提取
二进制鲁棒不变可扩展关键点算法
特征匹配
织物纹样识别
river cliff water pattern
feature extraction
binary robust invariant scalable key-points algorithm
feature matching
fabric pattern recognition