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• CMV, includes the general LBP implementation Archived 2014-11-28 at the Wayback Machine and many further extensions over LBP histogram in MATLAB.

• Python mahotas, an open source computer vision package which includes an implementation of LBPs.

• OpenCV's Cascade Classifiers support LBPs as of version 2.

• VLFeat, an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation.

• LBPLibrary is a collection of eleven Local Binary Patterns (LBP) algorithms developed for background subtraction problem. The algorithms were implemented in C++ based on OpenCV. A CMake file is provided and the library is compatible with Windows, Linux and Mac OS X. The library was tested successfully with OpenCV 2.4.10.

• BGSLibrary includes the original LBP implementation for motion detectioncite-ref-12[12] as well as a new LBP operator variant combined with Markov Random Fieldscite-ref-13[13] with improved recognition rates and robustness.

• dlib, an open source C++ library: implementation.

• scikit-image, an open source Python library. Provides a c-based python implementation for LBP

• Local ternary patterns

• Local Binary Pattern (LBP) methodology in Scholarpedia

• Census transform

cite-note-11. ↑ DC. He and L. Wang (1990), "Texture Unit, Texture Spectrum, And Texture Analysis", Geoscience and Remote Sensing, IEEE Transactions on, vol. 28, pp. 509 - 512.

cite-note-22. ↑ L. Wang and DC. He (1990), "Texture Classification Using Texture Spectrum", Pattern Recognition, Vol. 23, No. 8, pp. 905 - 910.

cite-note-33. ↑ T. Ojala, M. Pietikäinen, and D. Harwood (1994), "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions", Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582 - 585.

cite-note-44. ↑ T. Ojala, M. Pietikäinen, and D. Harwood (1996), "A Comparative Study of Texture Measures with Classification Based on Feature Distributions", Pattern Recognition, vol. 29, pp. 51-59.

cite-note-55. ↑ "An HOG-LBP Human Detector with Partial Occlusion Handling", Xiaoyu Wang, Tony X. Han, Shuicheng Yan, ICCV 2009

cite-note-66. ↑ C. Silva, T. Bouwmans, C. Frelicot, "An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos", VISAPP 2015, Berlin, Germany, March 2015.

cite-note-77. ↑ T. Bouwmans, C. Silva, C. Marghes, M. Zitouni, H. Bhaskar, C. Frelicot,, "On the Role and the Importance of Features for Background Modeling and Foreground Detection”, arXiv:1611.09099

cite-note-88. ↑ Barkan et al. "Fast High Dimensional Vector Multiplication Face Recognition." Proceedings of ICCV 2013

cite-note-99. ↑ Barkan et al. "Fast High Dimensional Vector Multiplication Face Recognition." Proceedings of ICCV 2013

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