Extreme Feature Regions for Image Matching

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dc.contributor.authorFan, Baijiangen_US
dc.contributor.authorRao, Yunboen_US
dc.contributor.authorPu, Jiansuen_US
dc.contributor.authorDeng, Jianhuaen_US
dc.contributor.editorFu, Hongbo and Ghosh, Abhijeet and Kopf, Johannesen_US
dc.date.accessioned2018-10-07T14:32:27Z
dc.date.available2018-10-07T14:32:27Z
dc.date.issued2018
dc.description.abstractExtreme feature regions are increasingly critical for many image matching applications on affine image-pairs. In this paper, we focus on the time-consumption and accuracy of using extreme feature regions to do the affine-invariant image matching. Specifically, we proposed novel image matching algorithm using three types of critical points in Morse theory to calculate precise extreme feature regions. Furthermore, Random Sample Consensus (RANSAC) method is used to eliminate the features of complex background, and improve the accuracy of the extreme feature regions. Moreover, the saddle regions is used to calculate the covariance matrix for image matching. Extensive experiments on several benchmark image matching databases validate the superiority of the proposed approaches over many recently proposed affine-invariant SIFT algorithms.en_US
dc.description.sectionheadersVisual Content Matching and Retrieval
dc.description.seriesinformationPacific Graphics Short Papers
dc.identifier.doi10.2312/pg.20181286
dc.identifier.isbn978-3-03868-073-4
dc.identifier.pages81-84
dc.identifier.urihttps://doi.org/10.2312/pg.20181286
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pg20181286
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectImage processing
dc.subjectimage
dc.subjectmatching
dc.subjectrandom sample consensus
dc.subjectaffine invariant
dc.titleExtreme Feature Regions for Image Matchingen_US
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