An Underwater Image Real-Time Registration Approach Based on SURF

Article Preview

Abstract:

In order to meet the demands of real-time performance and robustness for underwater image registration, a novel image registration method based on the SURF (Speeded-Up Robust Features) algorithm is proposed. During the image acquisition process, noise was generated inevitably because of many influencing factors such as atmospheric turbulence, camera defocus during image capturing or relative motion between the camera and the object. Firstly, median filter method was involved during the image preprocessing for underwater image contrast enhancement. Secondly, the SURF algorithm was used to obtain the interest points of the reference and registering images, and the nearest neighbor method was applied to search for coarse matching points. To obtain the precise matching points, the dominant orientations of the coarse matching points were used to eliminate the mismatching points. Finally, the precise matching points were adapted to calculate the mapping relationship between the registering and reference images, the bilinear interpolation method was applied to resample the registering image, and then the registered image was obtained. Experimental results indicated that the proposed preprocessing methods obviously enhanced the image quality, and the introduced image registration approach effectively improved the real-time performance and guaranteed the robustness at the same time.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

888-893

Citation:

Online since:

October 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] B. Zitova, J. Flusser, Image registration methods: a survey, Image and Vision Computing 21, 2003: 977–1000.

DOI: 10.1016/s0262-8856(03)00137-9

Google Scholar

[2] Q. Li, G. Wang, J. Liu, et al, Robust scale-invariant feature matching for remote sensing image registration, IEEE Geoscience and Remote Sensing Letters, 6(2), 2009: 287-291.

DOI: 10.1109/lgrs.2008.2011751

Google Scholar

[3] C. Harris, M. Stephens, Combined Corner and Edge Detection, Proceedings of The Fourth Alvey Vision Conference, UK, Alvey vision conference, 1988: 147-151.

DOI: 10.5244/c.2.23

Google Scholar

[4] T. Lindeberg, Feature Detection with Automatic Scale Selection, International Journal of Computer Vision, 30(2), 1988: 79-116.

Google Scholar

[5] D. Lowe, Object Recognition from Local Scale-Invariant Features, International Conference on Computer Vision, Corfu, Greece, September 1999: 1150-1157.

DOI: 10.1109/iccv.1999.790410

Google Scholar

[6] D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, 60(2), 2004: 91-110.

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[7] H. Bay, A. Ess, T. Tuytelaars, et al, Speeded-Up Robust Features(SURF), Computer Vision and Image Understanding, 110(3), 2008: 346-359.

DOI: 10.1016/j.cviu.2007.09.014

Google Scholar

[8] Y. Xie, X. Li, J. Lu, et al,Underwater Images Real-Time Registration Method Based on SURF, Journal of Computer-Aided Design & Computer Graphics, 22(12), 2010: 2215-2220.

Google Scholar

[9] Y. Shi, Improved image registration based on SURF, University of Electronic Science and Technology of China, 2008: 27-44.

Google Scholar

[10] R. Garcia, T. Nicosevici, X. Cuf, On the way to solve lighting problems in underwater imaging, Proceedings of IEEE Oceans Conference Record, Washington D C: IEEE Computer Society Press, 2002: 1018-1024.

DOI: 10.1109/oceans.2002.1192107

Google Scholar