Combining SIFT and individual entropy correlation coefficient for image registration

Gan Liu*, Shengyong Chen, Xiaolong Zhou, Xiaoyan Wang, Qiu Guan, Hui Yu

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Abstract

    Image registration is an important topic in many fields including industrial image analysis systems, medical and remote sensing. To improve the registration accuracy, an image registration method that combines scale invariant feature transform and individual entropy correlation coefficient (SIFTIECC) is proposed in this paper. First, scale invariant feature transform algorithm is applied to extract feature points to construct a transformation model. Then, a rough registration image is obtained according to the transformation model. The individual entropy correlation coefficient is used as the similarity measure to refine the rough registration image. Finally, the experimental results show the superior performance of the proposed SIFT-IECC registration method by comparing with the state-of-the-art methods.

    Original languageEnglish
    Title of host publicationPattern Recognition - 6th Chinese Conference, CCPR 2014, Proceedings, Part II
    EditorsShutao Li, Chenglin Liu, Yaonan Wang
    PublisherSpringer Verlag
    Pages128-137
    Number of pages10
    Volume484
    ISBN (Electronic)978-3662456422
    DOIs
    Publication statusPublished - Nov 2014
    Event6th Chinese Conference on Pattern Recognition - Changsha, China
    Duration: 17 Nov 201419 Nov 2014

    Publication series

    NameCommunications in Computer and Information Science
    Volume484
    ISSN (Print)1865-0929

    Conference

    Conference6th Chinese Conference on Pattern Recognition
    Abbreviated titleCCPR 2014
    Country/TerritoryChina
    CityChangsha
    Period17/11/1419/11/14

    Keywords

    • image registration
    • individual entropy correlation coefficient
    • scale invariant feature transform

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