Abstract
In this paper, we present an approach for extraction of texture features of underwater images using Robust Local Binary Pattern (RLBP) descriptor. The literature survey reveals that the texture parameters that remain constant for the scene patch for the whole underwater image sequence. Therefore, we proposed technique to extract the texture features and these features can be used for object recognition and tracking. The underwater images suffer from image blurring and low contrast and performance of feature extractors is very less if we employ directly. Thus, we propose a novel image enhancement technique which is combination of different individual filters such as homomorphic filtering, curvelet denoising and LBP based Diffusion. We employ DoG based feature detector, for each detected interest point, the texture description is extracted using RLBP feature descriptor. The proposed feature extraction technique is compared and evaluated extensively with well known feature extractors using datasets acquired in underwater environment.
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Nagaraja, S., Prabhakar, C.J., Kumar, P.U.P. (2015). Extraction of Texture Based Features of Underwater Images Using RLBP Descriptor. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_29
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DOI: https://doi.org/10.1007/978-3-319-12012-6_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12011-9
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