Abstract
Popular registration methods can be applying into multimodal images, such as Harris-PIIFD, SURF-RPM, GMM, GDB-ICP and so on. There exist some challenges in existing multimodal image registration techniques: (1) They fail to register image pairs with some significantly different content, illumination and texture changes; (2) They fail to register image pairs with too small overlapping or too much noise. To address these problem, this paper improves the multimodal registration by contribute a novel robust framework SURF-PIIFD-BBF-VFC (SPBV). The SURF-PIIFD method can provide enough repeatable and reliable local features; the bilateral matching method and vector field consensus (VFC) can establish robust point correspondences of two point sets. For evaluation, we compare the performance of the proposed SPBV with two existing methods Harris-PIIFD and SURF-RPM on two multimodal data sets. The results indicate that our SPBV method outperforms the existing methods and it is robust to low quality and small overlapping multimodal images.
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Acknowledgement
This work was supported by the Shanghai Innovation Action Project of Science and Technology (15DZ1101202) and the National Key Technology Support Program of China (No. 2015BAF17B00).
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Wang, X., Liu, X., Chen, Y., Zhou, Z. (2017). A Robust Method for Multimodal Image Registration Based on Vector Field Consensus. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_12
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