文献类型：期刊文献

中文题名：Fast Image Correspondence with Global Structure Projection

英文题名：Fast Image Correspondence with Global Structure Projection

作者：Lin, Qing-Liang[1];Sheng, Bin[1];Shen, Yang[1];Xie, Zhi-Feng[1];Chen, Zhi-Hua[2];Ma, Li-Zhuang[1]

机构：[1]Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China;[2]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2012

卷号：27

期号：6

起止页码：1281

中文期刊名：Journal of Computer Science & Technology

外文期刊名：JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY

收录：CSTPCD;;Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000311264700019)】；CSCD:【CSCD2011_2012】；PubMed;

基金：This work was supported by the National Natural Science Foundation of China under Grant Nos. 61133009. 61073089, and the Innovation Program of the Science and Technology Commission of Shanghai Municipality of China under Grant No. 10511501200.

语种：英文

中文关键词：object recognition;image correspoadence;structure projection;flat object

外文关键词：object recognition; image correspondence; structure projection; flat object

摘要：This paper presents a method for correspondence. This technique works by two recognizing images with steps： reference keypoint flat objects based on global keypoint structure selection and structure projection. The using of global keypoint structure is an extension of an orderless bag-of-features image representation, which is utilized by the proposed matching technique for computation efficiency. Specifically, our proposed method excels in the dataset of images containing ＂flat objects＂ such as CD covers, books, newspaper. The efficiency and accuracy of our proposed method has been tested on a database of nature pictures with flat objects and other kind of objects. The result shows our method works well in both occasions.
This paper presents a method for recognizing images with flat objects based on global keypoint structure correspondence. This technique works by two steps: reference keypoint selection and structure projection. The using of global keypoint structure is an extension of an orderless bag-of-features image representation, which is utilized by the proposed matching technique for computation efficiency. Specifically, our proposed method excels in the dataset of images containing "flat objects" such as CD covers, books, newspaper. The efficiency and accuracy of our proposed method has been tested on a database of nature pictures with flat objects and other kind of objects. The result shows our method works well in both occasions.