Titlebook: Computer Vision Using Local Binary Patterns; Matti Pietikäinen,Abdenour Hadid,Timo Ahonen Book 2011 Springer-Verlag London Limited 2011 Co

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Texture Classification and Segmentationts involving LBP descriptors. An unsupervised method for texture segmentation using LBP and contrast (LBP/C) distributions is presented in the second part of the chapter. This method has become very popular, and many variants of it have been proposed, for example for color-texture segmentation and segmentation of remotely sensed images.

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Recognition and Segmentation of Dynamic Texturesent recognition results are obtained for different test databases providing state-of-the-art performance. The segmentation method extends the unsupervised segmentation method presented in Chap. . into spatiotemporal domain. It provides very promising results with less computational complexity than most other methods.

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Background Subtraction This chapter presents a robust texture-based method for modeling the background and detecting moving objects, obtaining state-of-the-art performance. The method has been successfully used in a multi-object tracking system, for example.

Repatriate

LBP in Different Applicationsent image analysis problems and applications around the world. Among the most important areas of application are face analysis, biometrics, biomedical image analysis, industrial inspection and video analysis. This chapter presents a brief introduction to some representative papers from different application areas.

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Credence

Computing Models for Faint-Galaxy Samplests involving LBP descriptors. An unsupervised method for texture segmentation using LBP and contrast (LBP/C) distributions is presented in the second part of the chapter. This method has become very popular, and many variants of it have been proposed, for example for color-texture segmentation and segmentation of remotely sensed images.

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