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Visible and infrared image registration based on region features and edginess

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Abstract

Visible and infrared image registration is required for multi-sensor fusion and cooperative processing. However, traditional single-sensor image registration methods are generally not feasible as multi-sensor images are often loosely related and show different properties in imaging. This paper presents a coarse-to-fine procedure for registering visible and infrared images based on stable region features and edginess. Zernike moments are used to describe salient region features for a coarse registration, and an entropy optimal process based on edginess is used to refine the registration to achieve a more accurate result. Experiments show that the proposed method provides more robust and accurate registration than the existing methods.

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References

  1. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  2. Chen, H.M., Varshney, P.K., Arora, M.K.: Performance of mutual information similarity measure for registration of multitemporal remote sensing images. IEEE Trans. Geosci. Remote Sens. 41(11), 2445–2454 (2003)

    Article  Google Scholar 

  3. Chen, J., Tian, J.: Real-time multi-modal rigid registration based on a novel symmetric-sift descriptor. Prog. Nat. Sci. Mater. Int. 19(5), 643–651 (2009)

    Article  Google Scholar 

  4. Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)

    Article  Google Scholar 

  5. Chong, C.W., Raveendran, P., Mukundan, R.: Translation invariants of Zernike moments. Pattern Recognit. 36(8), 1765–1773 (2003)

    Article  MATH  Google Scholar 

  6. Gong, M., Zhao, S., Jiao, L., Tian, D., Wang, S.: A novel coarse-to-fine scheme for automatic image registration based on sift and mutual information. IEEE Trans. Geosci. Remote Sens. 52(7), 4328–4338 (2014)

    Article  Google Scholar 

  7. Han, J., Pauwels, E.J., De Zeeuw, P.: Visible and infrared image registration in man-made environments employing hybrid visual features. Pattern Recognit. Lett. 34(1), 42–51 (2013)

    Article  Google Scholar 

  8. Heikkilȧ, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognit. 42(3), 425–436 (2009)

    Article  MATH  Google Scholar 

  9. Heo, Y.S., Lee, K.M., Lee, S.U.: Robust stereo matching using adaptive normalized cross-correlation. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 807–822 (2011)

    Article  Google Scholar 

  10. Hrkać, T., Kalafatić, Z., Krapac, J.: Infrared-visual image registration based on corners and hausdorff distance. In: Scandinavian Conference on Image Analysis, pp. 383–392. Springer, Berlin (2007)

  11. Kim, Y.S., Lee, J.H., Ra, J.B.: Multi-sensor image registration based on intensity and edge orientation information. Pattern Recognit. 41(11), 3356–3365 (2008)

    Article  MATH  Google Scholar 

  12. Lee, J.H., Kim, Y.S., Lee, D., Kang, D.G., Ra, J.B.: Robust CCD and IR image registration using gradient-based statistical information. IEEE Signal Process. Lett. 17(4), 347–350 (2010)

    Article  Google Scholar 

  13. Li, B., Wang, X., Yang, H.: Registration of multi-sensor images based on enhanced star algorithm. In: 2015 8th International Congress on Image and Signal Processing (CISP), pp. 381–386. IEEE (2015)

  14. Maurer, C., Maciunas, R.J., Fitzpatrick, J.M.: Registration of head CT images to physical space using a weighted combination of points and surfaces [image-guided surgery]. IEEE Trans. Med. Imaging 17(5), 753–761 (1998)

    Article  Google Scholar 

  15. Misra, I., Moorthi, S.M., Dhar, D., Ramakrishnan, R.: An automatic satellite image registration technique based on harris corner detection and random sample consensus (RANSAC) outlier rejection model. In: 2012 1st International Conference on Recent Advances in Information Technology (RAIT), pp. 68–73. IEEE (2012)

  16. Petrović, V.: Subjective tests for image fusion evaluation and objective metric validation. Inf. Fusion 8(2), 208–216 (2007)

    Article  Google Scholar 

  17. Sedaghat, A., Mokhtarzade, M., Ebadi, H.: Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 49(11), 4516–4527 (2011)

    Article  Google Scholar 

  18. Shen, X., Xu, L., Zhang, Q., Jia, J.: Multi-modal and multi-spectral registration for natural images. In: European Conference on Computer Vision, pp. 309–324. Springer, Berlin (2014)

  19. Teh, C.H., Chin, R.T.: On image analysis by the methods of moments. IEEE Trans. Pattern Anal. Mach. Intell. 10(4), 496–513 (1988)

    Article  MATH  Google Scholar 

  20. Wang, P., Wang, P., Qu, Z.G., Gao, Y.H., Shen, Z.K.: A novel algorithm for visual and IR image registration based on hough transform. In: 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM), pp. 1–4. IEEE (2011)

  21. Ye, Y., Shan, J.: A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences. ISPRS J. Photogramm. Remote Sens. 90(3), 83–95 (2014)

    Article  Google Scholar 

  22. Yi, Z., Zhiguo, C., Yang, X.: Multi-spectral remote image registration based on sift. Electron. Lett. 44(2), 107–108 (2008)

    Article  Google Scholar 

  23. Zhao, D., Yang, Y., Ji, Z., Hu, X.: Rapid multimodality registration based on MM-SURF. Neurocomputing 131, 87–97 (2014)

    Article  Google Scholar 

  24. Zhou, J., Liu, Q.: A combined similarity measure for multimodal image registration. In: 2015 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–5. IEEE (2015)

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61231016, 61303123, 61273265), the Natural Science Foundation of Shaanxi Province (No. 2015JQ6256), the Fundamental Research Funds for the Central Universities (No. 3102015JSJ0008).

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Yanjia Chen, Xiuwei Zhang, Yanning Zhang & Zhipeng Fu

  2. Department of Computer Science and Information Systems, Birkbeck College, University of London, London, UK

    Stephen John Maybank

  3. Henan University of Science and Technology, Luoyang, China

    Zhipeng Fu

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  1. Yanjia Chen
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  2. Xiuwei Zhang
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  3. Yanning Zhang
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  4. Stephen John Maybank
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  5. Zhipeng Fu
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Correspondence to Xiuwei Zhang.

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Chen, Y., Zhang, X., Zhang, Y. et al. Visible and infrared image registration based on region features and edginess. Machine Vision and Applications 29, 113–123 (2018). https://doi.org/10.1007/s00138-017-0879-6

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  • DOI: https://doi.org/10.1007/s00138-017-0879-6

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