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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Combining SIFT and Individual Entropy Correlation Coefficient for Image Registration
verfasst von : Gan Liu, Shengyong Chen, Xiaolong Zhou, Xiaoyan Wang, Qiu Guan, Hui Yu
Erschienen in: Pattern Recognition
Verlag: Springer Berlin Heidelberg
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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 (SIFT-IECC) 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.