Self-similar texture characterization using a Fourier-domain maximum likelihood estimation method
Pattern Recognition Letters
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using multiresolution Markov random field models
Pattern Recognition Letters
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
DCT histogram optimization for image database retrieval
Pattern Recognition Letters
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local binary patterns for a hybrid fingerprint matcher
Pattern Recognition
A novel extended local-binary-pattern operator for texture analysis
Information Sciences: an International Journal
Description of interest regions with local binary patterns
Pattern Recognition
Multiscale texture classification using dual-tree complex wavelet transform
Pattern Recognition Letters
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Indirect immunofluorescence image classification using texture descriptors
Expert Systems with Applications: An International Journal
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The local binary pattern (LBP) operator is a very effective multi-resolution texture descriptor that can be applied in many image processing applications. However, existing LBP operators can not use the information of non-uniform patterns efficiently and they are also sensitive to noise. This paper proposes a noise tolerant extension of LBP operators to extract statistical and structural image features for efficient texture analysis. The proposed LBP operator uses a circular majority voting filter and suitable rotation-invariant labeling scheme to obtain more regular uniform and non-uniform patterns that have better discrimination ability and more robustness against noise. Experimental results on the Brodatz, CUReT and MeasTex databases show the improvement of the proposed LBP operator performance, especially when a large number of neighbors are used for extracting texture patterns.