Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A massively parallel face recognition system
EURASIP Journal on Embedded Systems
Description of interest regions with local binary patterns
Pattern Recognition
Texture based classification of hyperspectral colon biopsy samples using CLBP
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Fabric defect detection based on adaptive local binary patterns
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Pyramid-based multi-structure local binary pattern for texture classification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
On the Occurrence Probability of Local Binary Patterns: A Theoretical Study
Journal of Mathematical Imaging and Vision
Automated Marsh-like classification of celiac disease in children using local texture operators
Computers in Biology and Medicine
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
An active contour model guided by LBP distributions
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Information Sciences: an International Journal
Virus texture analysis using local binary patterns and radial density profiles
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Noisy Iris Recognition Integrated Scheme
Pattern Recognition Letters
Global localization with non-quantized local image features
Robotics and Autonomous Systems
Biometric cryptosystem based on discretized fingerprint texture descriptors
Expert Systems with Applications: An International Journal
Neighborhood Correlation Analysis for Semi-paired Two-View Data
Neural Processing Letters
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Recently, a nonparametric approach to texture analysis has been developed; in which the distributions of simple texture measures based on local binary patterns (LBP) are used for texture description. The basic LBP encodes 256 simple feature detectors in a single 3x3 operator. This paper shows that a properly selected subset of patterns encoded in LBP forms an efficient and robust texture description, which can achieve better classification rates in comparison with the whole LBP histogram. Experiments on classification of textures from the Columbia-Utrecht (CURET) database demonstrate the robustness of the approach.