Abstract
Submerged objects discovery has been broadly completed by utilizing an acoustic device like side-scan sonar (SSS) which captured the pictures of seabed silt and structures. Such pictures are known as SSS image and it is of low contrast due to pixels intensity exists wider in a restricted range of the histogram. Therefore, the items in this sort of pictures are not clear and distinct. This paper presents fuzzy c-means (FCM) with local binary pattern. (LBP) and empirical mode decomposition (EMD) combined for enhancement of the SSS images. In this, EMD is used for image enhancement and FCM utilized to segment the image in order to extract the feature of the SSS image and Local Binary Pattern (LBP) algorithm is used to find texture of the enhanced image. The EMD is a versatile algorithm helpful for breaking down nonlinear and non-stationary signals. Thereby, intrinsic mode functions (IMF) of the three shading channels (Red, Green, and Blue) is calculated independently. Then, all the three channels IMFs are combined with ideal weights. It induces that the pixels of enhanced SSS images are uniformly distributed in the histogram range and also improved the color contrast problem. Therefore, the proposed approach has better density upgrade and the ocean bed structure will be fortified concentrate and dregs easily.
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Cervenka P, de Moustier C (1993) Side scan image processing techniques. IEEE J Oceanic Eng 18(2):108–122
Thakur V, Tripathi N (2010) On the way towards efficient enhancement of multi-channel underwater images. Int J Appl Eng Res 5(5):895–903
Iqbal K, Abdul Salam R, Osman A, Talib A (2007) Underwater image enhancement using an integrated colour model. IAENG Int J Comput Sci 34:2–8
Hasikin K, Isa NAM (2014) Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images. SIViP 8(8):1591–1603
Santhi K, Banu RW (2015) Contrast enhancement by modified octagon histogram equalization. SIViP 9(1):73–87
Thangaswamy SS, Kadarkarai R, Thangasamy SRR (2013) Developing an efficient techniques for satellite image denoising and resolution enhancement for improving classification accuracy. J Electron Imaging 22(1):1–7
Garcia R, Nicosevici T, Cufi X (2002) On the way to solve lightning problems in underwater imaging. In: IEEE OCEANS conference, pp 1018–1024
Hariharan H, Gribok A, Abidi M, Koschan A (2006) Image fusion and enhancement via empirical mode decomposition. J Pattern Recogn Res 1(1):16–32
Tas yapı Çelebi A, Ertürk S (2010) Empirical mode decomposition based visual enhancement of underwater images. In: 2nd international conference on image processing theory tools and applications (IPTA), Paris, pp 221–224
Bazeille S, Quidu I, Jaulin L, Malkasse JP (2006) Automatic underwater image pre-preprocessing. In: Proceedings of the characterisation du Milieu Marin (CMM’06) Brest, France, Oct 2006, pp 16–19
Padmavathi G, Subashini P, Kumar MM, Thakur SK (2010) Comparison of filters used for underwater image pre-processing. IJCSNS Int J Comput Sci Net Secur 10(1):58–65
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert Spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London A 454:903–995
Liu Z, Liao Z, Sang E (2005) Noise removal of sonar image using empirical mode decomposition. Proc SPIE 6044:60440N-1–60440N-9
Linderhed A (2004) Image compression based on empirical mode decomposition. In: Proceeding of SSAB Symposium Image Analysis, Uppsala, pp 110–113
Janusauskas A, Jurkonis R, Lukosevicius A, Kurapkiene S, Paunksnis A (2005) The empirical mode decomposition and the discrete wavelet transform for detection of human cataract in ultrasound signals. Inf Lith Acad Sci 16(4):541–556
Taşyapı Çelebi A, Ertürk S (2010) Target detection in sonar images using empirical mode decomposition and morphology. Undersea Defence Technology (UDT) Europe, pp 22–24
Sowmyashree MS, Bekel SK, Sneha R, Priyanka N (2014) A survey on the various underwater image enhancement techniques. Int J Eng Sci Invention 40–45. , ISSN: 2319-6734
http://www.blacklaserlearning.com/images/pauline_marie_web.jpg
http://kleinmarinesystems.com/products/side-scan-sonar/system-3900/#prettyPhoto[gallery2]/0/
http://static.seattletimes.com/wp-content/uploads/2007/08/2003847265-300x0.jpg
Demir B, Ertürk S (2008) Empirical mode decomposition pre-process for higher accuracy hyperspectral image classification. In: International conference on geoscience and remote sensing symposium, Boston, Massachusetts, U.S.A, pp. II-939–II-941
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Somasekar, M., Sakthivel Murugan, S. (2019). Feature Extraction of Underwater Images by Combining Fuzzy C-Means Color Clustering and LBP Texture Analysis Algorithm with Empirical Mode Decomposition. In: Murali, K., Sriram, V., Samad, A., Saha, N. (eds) Proceedings of the Fourth International Conference in Ocean Engineering (ICOE2018). Lecture Notes in Civil Engineering, vol 22. Springer, Singapore. https://doi.org/10.1007/978-981-13-3119-0_26
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DOI: https://doi.org/10.1007/978-981-13-3119-0_26
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