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[主讲人: Research Scientist Xiaopeng Hong] [时间: 2014-06-05 10:00:00]
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.The LBP has been successfully applied to numerous tasks in computer vision, such as static and dynamic texture classification, face recognition, facial expression recognition, action recognition, gait and gesture recognition, and visual speech recognition.
This talk will briefly introduce the principle of LBP as well as some of its most important variants, and also discuss about the recent work on how to address the numerical constraint of LBP.
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