Abstract
In underwater environment, the underwater images would have low S/N and the detail is fuzzy due to scattering and absorption of a variety of suspended matter in water and the water itself and uneven lightness. If traditional methods are used to dispose underwater images directly, it is unlikely to obtain satisfactory results. As the mission of the vision system of autonomous underwater vehicle (AUV), it should deal with the information about the object in the complex environment rapidly and exactly for AUV to use the obtained result for the next task. So, aiming at realizing the clustering quickly on the basis of providing a high qualified segmentation of an underwater image, a novel interval fuzzy c-means algorithm based on gradient edge for underwater image segmentation is proposed. Experimental results indicate that the novel algorithm can get a better segmentation result and the processing time of each image is reduced and enhance efficiency and satisfy the request of highly real-time effectiveness of AUV.
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Wang, S., Xu, Y., Wan, L. (2011). An Interval Fuzzy C-means Algorithm Based on Edge Gradient for Underwater Optical Image Segmentation. In: Lin, S., Huang, X. (eds) Advances in Computer Science, Environment, Ecoinformatics, and Education. CSEE 2011. Communications in Computer and Information Science, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23321-0_43
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DOI: https://doi.org/10.1007/978-3-642-23321-0_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23320-3
Online ISBN: 978-3-642-23321-0
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