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Multimodal image matching based on multimodality robust line segment descriptor
Zhao CY(赵春阳); Zhao HC(赵怀慈); Lv JF(吕进锋); Sun SJ(孙世杰); Li B(李波)
Department光电信息技术研究室
Source PublicationNeurocomputing
ISSN0925-2312
2016
Volume177Pages:290-303
Indexed BySCI ; EI
EI Accession number20160701932645
WOS IDWOS:000370085800028
Contribution Rank1
KeywordMultimodal Image Matching Multimodality Robust Line Segment Descriptor Multimodality Robust Line Segment Mrlsd Matching
AbstractAlthough a number of local feature-based methods have been proposed, the multimodality matching is still a challenging problem in object recognition, remote sensing and medical image processing where the image contrast is significantly different. The local feature-based multimodality matching method is usually intensity-based, so the matching performance is not good enough because intensity-based method is sensitive to contrast variations. In order to solve these problems, we propose a novel Multimodality Robust Line Segment Descriptor (MRLSD) and develop a MRLSD matching method. The proposed method generates MRLSD descriptors based on extracted highly equivalent corners and line segments for two multimodal images, and then performs image matching by measuring the similarity of corresponding descriptors over two images. The proposed corner and line segment extraction method is based on local phase and direction information, and is insensitive to contrast variations, so the MRLSD descriptor is robust to modality variations. The MRLSD descriptor is rotation invariant by selecting circular feature sub-regions and projecting feature vectors to radial direction. The MRLSD descriptor achieves scale invariance by adjusting the radius of circular feature region according to the scale. Experimental results indicate that the proposed method achieves higher precision and repeatability than several state-of-the-art local feature-based multimodality matching methods, and also demonstrate its robustness to multimodal images.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence
WOS KeywordREGISTRATION ; INVARIANT ; FEATURES ; SURF
WOS Research AreaComputer Science
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/17647
Collection光电信息技术研究室
Corresponding AuthorZhao CY(赵春阳)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang, China
3.The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang, China
4.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Zhao CY,Zhao HC,Lv JF,et al. Multimodal image matching based on multimodality robust line segment descriptor[J]. Neurocomputing,2016,177:290-303.
APA Zhao CY,Zhao HC,Lv JF,Sun SJ,&Li B.(2016).Multimodal image matching based on multimodality robust line segment descriptor.Neurocomputing,177,290-303.
MLA Zhao CY,et al."Multimodal image matching based on multimodality robust line segment descriptor".Neurocomputing 177(2016):290-303.
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