Combining SIFT and Individual Entropy Correlation Coefficient for Image Registration
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.
KeywordsImage registration Scale invariant feature transform Individual entropy correlation coefficient
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- 3.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)Google Scholar
- 6.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)Google Scholar
- 7.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)Google Scholar
- 8.Goshtasby, A.A.: 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications. Wiley (2005)Google Scholar
- 14.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)Google Scholar
- 15.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)Google Scholar