A Dynamic Mode Decomposition Based Edge Detection Method for Art Images

被引:17
|
作者
Bi, Chongke [1 ]
Yuan, Ye [1 ]
Zhang, Ronghui [2 ]
Xiang, Yiqing [1 ]
Wang, Yuehuan [1 ]
Zhang, Jiawan [1 ]
机构
[1] Tianjin Univ, Sch Comp Software, Tianjin 300350, Peoples R China
[2] Sun Yat Sen Univ, Sch Engn, Guangdong Key Lab Intelligent Transportat Syst, Guangzhou 510275, Guangdong, Peoples R China
来源
IEEE PHOTONICS JOURNAL | 2017年 / 9卷 / 06期
基金
中国国家自然科学基金;
关键词
Image processing; image edge detection; filtering; optical filters; optical engineering; OPERATORS; FLOW;
D O I
10.1109/JPHOT.2017.2766881
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Edge detection is a widely used feature extraction method in various fields, such as image processing, computer vision, machine vision, and so forth. However, it is still a challenging task to extract edges from art images, due to the false edge, shadow, and double lines of art images. In this paper, we propose a dynamic mode decomposition algorithm (DMD) based method for edge detection of art images. This is achieved by proposing a new color space based denoise method to deal with the shadow issue. Then, the false edge and double lines can be resolved by employing DMD method, which can be used to extract sparse features from the denoised images. Here, the sparse features have been enhanced by a new designed eight direction gradient operator. Finally, the effectiveness of our method will be demonstrated through detecting the edges of three classical types of art images (Comic, Oil Painting, and Printmaking).
引用
收藏
页数:13
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