Abstract
According to the characteristics of sonar image data with big data feature, In order to accurately detect underwater objects of sonar image, a novel adaptive threshold FCM (Fuzzy Clustering Algorithm, FCM) based on frequency domain and time domain is proposed. Based on the relationship between sonar image data and big data, Firstly, wavelet de-noising method is used to smooth noise. After de-noising, the sonar image is blocked and each sub-block region is processed by two-dimensional discrete Fourier transform, their maximum amplitude spectrum used as frequency domain character, then time domain of mean and standard deviation, frequency domain of maximum amplitude spectrum are taken for character to complete block k-means clustering, the initial clustering center is determined, after that made use of FCM on sonar image detection, based on clustered image, adaptive threshold is constructed by the distribution of sonar image sea-bottom reverberation region, and final detection results of sonar image are completed. The comparison different experiments demonstrate that the proposed algorithm get good detection precision and adaptability.
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References
Liu, G., Bian, H., Shi, H.: Sonar Image Segmentation based on an Improved Level Set Method. In: Proceedings of International Conference on Medical Physics and Biomedical Engineering, pp. 71168–71175 (2012)
Ye, X.-F., Zhang, Z.-H., Liu, P.X., Guan, H.-L.: Sonar image segmentation based on GMRF and level-set models. Ocean Engineering 37(10), 891–901 (2010)
Wang, X., Liu, G., Li, L., Liu, Z.: A Novel Quantum-inspired Algorithm for Edge Detection of Sonar Image. In: The 33rd Chinese Control Conference, pp. 401–409 (2014)
Wu, J.: Wavelet domain de-noising method based on multistage median filtering. The Journal of China Universities of Posts and Telecommunications 20(2), 113–119 (2013)
Cinque, L., Foresti, G., Lombardi, L.: A clustering fuzzy approach for image segmentation. Journal of Manufacturing Automation 37(9), 1797–1807 (2004)
Chen, Y.T.: Image de-noising and quality measurements by using filtering and wavelet based techniques. AEU-International Journal of Electronics and Communications 68(8), 699–705 (2014)
Wei, W.-Y.: New Ostu image segmentation based on intensity stretching on DWT fields. Journal of Northwest Normal University (Natural Science) 45(6), 46–48 (2009)
Lei, W., Xiufen, Y., Tian, W.: Segmentation algorithm of fuzzy clustering on sidescan sonar image. Journal of Huazhong University of Science & Technology (Natural Science Edition) 40(9), 25–29 (2012)
Ye, X.-F., Wang, X.-M., Zhang, Z.-H., Fang, C.: Study of sonar imagery segmentation algorithm by improved MRF parameter model. Journal of Harbin Engineering Unviersity 30(7), 768–774 (2009)
Tian, X.-D., Tong, J.-J., Liu, Z.: Background gray level distribution in sonar images. Technical Acoustics 25(5), 468–472 (2006)
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Wang, X., Liu, G., Li, L., Jiang, S. (2015). Detection of Underwater Objects by Adaptive Threshold FCM Based on Frequency Domain and Time Domain. In: Wang, H., et al. Intelligent Computation in Big Data Era. ICYCSEE 2015. Communications in Computer and Information Science, vol 503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46248-5_24
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DOI: https://doi.org/10.1007/978-3-662-46248-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46247-8
Online ISBN: 978-3-662-46248-5
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