摘要
为客观有效地识别局部图案轮廓,实现可选择性目标的提取,文章以皮影图案为研究对象,针对皮影图像局部细节丰富、色彩饱和度高而背景信息干扰较大的特点,设计了皮影图案轮廓的智能提取算法。首先,采用相对总变差模型进行噪声与主结构的分离,实现图像的平滑处理;然后,设计GrabCut算法,通过分析图案轮廓的边界紧密度指标,确定最优的超像素分割数量,实现局部图案的优化分割;最后,运用Canny算子对分割后的皮影图案进行了轮廓提取。通过6幅皮影图像的轮廓提取实验结果表明,提出的方法准确完整地实现了目标图案的轮廓提取,且图案分割结果的像素准确度(PA)均大于95%。
In order to identify the local pattern contour and extract the optional target,this paper takes the shadow pattern as the research object.According to the characteristics of rich local details,high color saturation and strong interference of background information in the shadow image,an intelligent contour extraction algorithm for shadow patterns was designed.First of all,in order to achieve image smoothing,the relative total variation model was used to separate the noise from the main structure.Then,the GrabCut algorithm was designed.By analyzing the boundary compactness index of the pattern contour,the optimal super-pixel segmentation quantity was determined to realize the optimal segmentation of the local pattern.Finally,the contour of the segmented shadow pattern was extracted by Canny operator.The contour extraction experiment results of 6 shadow images showed that the method proposed in this paper could extract the contour of target pattern accurately and completely,and the pixel accuracy(PA)of the pattern segmentation was greater than 95%.
作者
刘静
庄梅玲
石历丽
高婷
LIU Jing;ZHUANG Meiling;SHI Lili;GAO Ting(College of Textiles and Clothing,Qingdao University,Qingdao 266071,China;Clothing Department,Xi’an Academy of Fine Arts,Xi’an 710065,China)
出处
《丝绸》
CAS
CSCD
北大核心
2020年第11期20-27,共8页
Journal of Silk
基金
青岛大学研究项目(JXGG2019080)
陕西省科技厅项目(2016FP-07)。
