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 (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.
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References
Xing, C., Qiu, P.H.: Intensity-Based Image Registration by Nonparametric Local Smoothing. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(10), 2081–2092 (2011)
Oliveira, F.P.M., Tavares, J.: Medical image registration: a review. Computer Methods in Biomechanics and Biomedical Engineering 17(2), 73–93 (2014)
Cheng, D., Xie, S.Q., Hammerle, E.: A Robust Local Descriptor Method for Registering Maori Artefacts using Colour Images. In: International Conference on Information and Automation, vols. 1-3. IEEE, New York (2009)
Liang, J., Liu, X., Huang, K., Li, X., Wang, D., Wang, X.: Automatic Registration of Multisensor Images Using an Integrated Spatial and Mutual Information (SMI) Metric. IEEE Transactions on Geoscience and Remote Sensing 52(1), 603–615 (2014)
Yokoi, T., Soma, T., Shinohara, H., Matsuda, H.: Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images. Annals of Nuclear Medicine 18(8), 659–667 (2004)
Xu, H.L., Hua, G.R., Zhuang, J., Wang, S.A.: A Frequency Domain Approach to Fast and Accurate Image Registration. In: International Conference on Information and Automation, vols. 1-3. IEEE, New York (2009)
Hurtos, N., Cuf, X., Petillot, Y., Salvi, J., Robotics Society of, J.: Fourier-based Registrations for Two-Dimensional Forward-Looking Sonar Image Mosaicing. In: 25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5298–5305. IEEE (2012)
Goshtasby, A.A.: 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications. Wiley (2005)
Lee, E.S., Kang, M.G.: Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration. IEEE Transactions on Image Processing 12(7), 826–837 (2003)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Itou, T., Shinohara, H., Sakaguchi, K., Hashimoto, T., Yokoi, T., Souma, T.: Multimodal image registration using IECC as the similarity measure. Medical Physics 38(2), 1103–1115 (2011)
Lindeberg, T.: Scale-space theory: A basic tool for analyzing structures at different scales. Journal of Applied Statistics 21(1-2), 225–270 (1994)
Skerl, D., Likar, B., Fitzpatrick, J.M., Pernus, F.: Comparative evaluation of similarity measures for the rigid registration of multi-modal head images. Physics in Medicine and Biology 52(18), 5587–5601 (2007)
Moradi, M., Abolmaesumi, P.: Medical image registration based on distinctive image features from scale-invariant (SIFT) key-points. In: 19th International Congress and Exhibition on Computer Assisted Radiology and Surgery, vol. 1281, pp. 1292–1292. Elsevier (2005)
Suri, S., Schwind, P., Reinartz, P., Uhl, J.: Combining mutual information and scale invariant feature transform for fast and robust multisensor SAR image registration. In: Proceedings of the 75 ASPRS Annual Conference (2009)
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Liu, G., Chen, S., Zhou, X., Wang, X., Guan, Q., Yu, H. (2014). Combining SIFT and Individual Entropy Correlation Coefficient for Image Registration. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_14
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DOI: https://doi.org/10.1007/978-3-662-45643-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45642-2
Online ISBN: 978-3-662-45643-9
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