Computer Vision Using Local Binary Patterns

By Matti Pietikäinen, Abdenour Hadid, Guoying Zhao, Timo Ahonen. 2011

765 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Local binary features for texture classification: Taxonomy and experimental study (2017, methodology): “Of the many methods, Local Binary Pattern (LBP) methods [1] have emerged as one of the most prominent and widely-studied classes of texture features, such that a vast number of LBP variants has been proposed for a diverse range of problems including texture classification [2–4], dynamic texture reco”
Median Robust Extended Local Binary Pattern for Texture Classification (2016, methodology): “…LBP method has been successfully applied to many diverse problems including dynamic texture recognition, remote sensing, ﬁngerprint matching, visual inspection, image retrieval, biomedical image analysis, face image analysis, motion analysis, edge detection, and environment modeling [1], [7]–[11].”
LBP − T OP based countermeasure against face spooﬁng attacks (2012, background): “They can follow the normal, the uniform pattern (u2) or rotation invariant uniform pattern (riu2) approaches [10], for example.”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, methodology): “50 rectangular patches are sampled at random, and described with three rotation-invariant LBP histograms [108], respectively of the H, S and V channels, concatenated to form a single feature vector.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “[185] in 2011 provides an excellent overview of the theory of Local Binary Patterns (LBP) and the use in solving various kinds of problems in computer vision, especially in biomedical applications and biometric recognition systems.”
BSIF: Binarized statistical image features (2012, background): “Some of them, or their variants, have become increasingly popular also in other applications, like face identification [1, 2] and action recognition from videos [12].”