Sonar Image Registration Based on Improved PSO and Powell Hybrid Algorithm

Abstract:

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In order to further improve the accuracy of the sonar image registration, a novel hybrid algorithm was proposed. It proposed the normalized mutual information as similar estimation, drawn on multi-resolution data structure based on wavelet transform, a low precision solution was solved by improved PSO algorithm, which has strong global search capability, firstly and then a high precision solution was acquired by Powell method, which has strong local search capability. The hybrid algorithm is effective to overcome the fall of local maximum mutual information function; it also improves solution’s precision. Since the hybrid algorithm is the initial point of pre-treatment, an effective solution to the Powell method dependence on the initial point. Experiments reveal that the hybrid algorithm is efficiency and effectiveness.

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Edited by:

Ran Chen and Wen-Pei Sung

Pages:

1811-1815

DOI:

10.4028/www.scientific.net/AMR.490-495.1811

Citation:

D. Wang and H. Y. Bian, "Sonar Image Registration Based on Improved PSO and Powell Hybrid Algorithm", Advanced Materials Research, Vols. 490-495, pp. 1811-1815, 2012

Online since:

March 2012

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Price:

$35.00

[1] WANG Guang-tai, HU Shun-bo, LIU Chang-chun, et al. Multi-image registration based on entropy of normalized mutual information vector [J]. Computer Engineering and Applications, 2009, 45(22): 172-174.

[2] Jenkinson M, Smith S. A global optimization method for robust affine registration of brain im-ages[J]. Medical Image Analysis, 2001, 5(2): 143-156.

DOI: 10.1016/s1361-8415(01)00036-6

[3] Plattard D, Soret M, Troccaz J, et al. Patient set-up using portal images: 2D/2D image registration using mutual information[J]. Computer Aided Surgery, 2000, 5(4): 246-262.

DOI: 10.3109/10929080009148893

[4] Wachowiak M P. Similarity metrics and optimization for multimodal biomedical image registration[D]. Louisville, USA: University of Louisville, (2003).

[5] Studholme C, Hill D L G, Hawkes D J. An overlap invariant entropy measure of 3D medical i-mage alignment [J]. Pattern Recognition, 1999, 32(1): 71-86.

DOI: 10.1016/s0031-3203(98)00091-0

[6] Maes F, Collignon A E, Vandermeulen D, et al. Multimodality image registration bymaximization of mutual information [J]. IEEE Transactions on Medical Imaging, 1996, 16(2): 187-198.

DOI: 10.1109/42.563664

[7] Kennedy J, Eberhart R C. Particle swarm optimization[C]/Proceedings of IEEE International Conference on Neural Networks. IEEE Service Center, Piscataway, NJ, 1995: 1942-(1948).

[8] Eberhart R C, Kennedy J. A new optimizer using particles swarm theory[C]/Proc Sixth Intern-ational Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995: 39-43.

DOI: 10.1109/mhs.1995.494215

[9] M J Powell. An efficient method for finding the minimum of a function of several variables without calculating derivatives [J]. The Computer Journal, 1964, 7(2): 155-162.

DOI: 10.1093/comjnl/7.2.155

[10] Nunez J, Otazu X, Fors O, et al. Multiresolution-based image fusion with additivewavelet de-composition[J]. Optical Engineering, 2000, 39(8): 2075-(2082).

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