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||<tablewidth="659px" tableheight="200px"style="vertical-align: top; border: medium none;"> {{attachment:lbpmain.jpg||height="296px",width="294px"}} ||<style="vertical-align: top; border: medium none;">Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel with the value of the center pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its robustness to monotonic gray-scale changes caused, for example, by illumination variations. Another important property is its computational simplicity, which makes it possible to analyze images in challenging real-time settings.<<BR>><<BR>>For publications using LBP, see [[MVG/LBP_Bibliography|LBP Bibliography]]. || ||<tablewidth="659px" tableheight="200px"style="vertical-align: top; border: medium none;"> {{attachment:lbpmain.jpg||height="296px",width="294px"}} ||<style="vertical-align: top; border: medium none;">Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel with the value of the center pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its robustness to monotonic gray-scale changes caused, for example, by illumination variations. Another important property is its computational simplicity, which makes it possible to analyze images in challenging real-time settings.<<BR>><<BR>>For publications using LBP, see [[CMV/LBP_Bibliography|LBP Bibliography]]. ||
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[[MVG/Research/LBP/LBPShort|LBP methodology in short]] [[CMV/Research/LBP/LBPShort|LBP methodology in short]]
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[[MVG/Research/LBP/LBP_Slides|LBP slides]] [[CMV/Research/LBP/LBP_Slides|LBP slides]]
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[[MVG/Research/LBP/Demos|Demonstrations]] [[CMV/Research/LBP/Demos|Demonstrations]]
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[[MVG/Research/LBP/DoctoralTheses|Doctoral theses on LBP]] [[CMV/Research/LBP/DoctoralTheses|Doctoral theses on LBP]]
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[[MVG/Downloads/LBPSoftware|LBP software]] [[CMV/Downloads/LBPSoftware|LBP software]]

Local Binary Pattern (LBP)

lbpmain.jpg

Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel with the value of the center pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its robustness to monotonic gray-scale changes caused, for example, by illumination variations. Another important property is its computational simplicity, which makes it possible to analyze images in challenging real-time settings.

For publications using LBP, see LBP Bibliography.

LBP methodology in short

LBP in Scholarpedia

ICCV 2009 tutorial slides: Local texture descriptors in computer vision.

LBP slides

Demonstrations

Doctoral theses on LBP

LBP software


For more information, contact Matti Pietikäinen.

CMV/Research/LBP (last edited 2011-11-18 14:17:08 by WebMaster)