Texture analysis plays an important role in image processing. Due to the highly textured appearance of sonar images, texture analysis methods are a suitable choice for analyzing sea acoustic images. The local binary pattern operator is a very effective multi-resolution
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Texture analysis plays an important role in image processing. Due to the highly textured appearance of sonar images, texture analysis methods are a suitable choice for analyzing sea acoustic images. The local binary pattern operator is a very effective multi-resolution texture descriptor. This descriptor obtains appropriate information from changing brightness and image states. Although many extensions of local binary pattern have been proposed, existing local binary pattern operators are sensitive to noise. Also, sometimes they lead to the description of different structural patterns with homogeneous binary code, which inevitably reduce their discriminability. This research provides an overview of the local binary pattern method, which includes several of the newer variables. Then, to overcome the inefficiencies of various types of local binary patterns, a robust binary pattern framework called robust local binary pattern is presented, in which the value of each central pixel is replaced by the average gray intensity values of houses from a three by three square. The proposed method is a fast tool with high accuracy in classifying seabed images, and comparing the simulation results with other well-known methods shows the efficiency of the proposed algorithm.
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