A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification

By Timo Ojala, Matti Pietikäinen, Topi Mäenpää. International Conference on Advances in Pattern Recognition, 2001

364 citations recorded by the source corpus.

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Effective face frontalization in unconstrained images (2014, methodology): “We then trained eight linear SVM classifiers, one for each point, to recognize local appearances at each point, represented as LBP code [21, 22].”
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation (2014, background): “pixel in the center and the surrounding pixels [48,64,65].”
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns (2015, background): “define a novel mapping from intensities to illumination invariant, 3D spaces, based on the well-known Local Binary Patterns (LBP) feature transform [25, 27, 26].”
A deep look into radiomics (2021, background): “LBP is a texture descriptor, introduced by Ojala [29], which assigns a label, i.”
Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics (2011, positive): “A texture descriptor called Local Binary Patterns (LBP) [21], [22], [23] has been shown to be extremely effective for face recognition [24].”