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• W2293457052 abstract "Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption." @default.
• W2293457052 created "2016-06-24" @default.
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• W2293457052 date "2016-03-01" @default.
• W2293457052 modified "2023-10-16" @default.
• W2293457052 title "Median Robust Extended Local Binary Pattern for Texture Classification" @default.
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• W2293457052 doi "https://doi.org/10.1109/tip.2016.2522378" @default.
• W2293457052 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26829791" @default.
• W2293457052 hasPublicationYear "2016" @default.
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