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|a eng
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| 100 |
1 |
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|a Liu, Li
|e verfasserin
|4 aut
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| 245 |
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|a Median Robust Extended Local Binary Pattern for Texture Classification
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|c 2016
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|a Date Completed 01.07.2016
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|a Date Revised 21.03.2022
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Lao, Songyang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fieguth, Paul W
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Yulan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Xiaogang
|e verfasserin
|4 aut
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| 700 |
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|a Pietikäinen, Matti
|e verfasserin
|4 aut
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| 773 |
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|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 25(2016), 3 vom: 01. März, Seite 1368-81
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|g number:3
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|g pages:1368-81
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