基于局部二值模式的纹理特征提取方法综述

N. Kaur, Nahida Nazir, Manik
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引用次数: 7

摘要

在图像处理领域,需要对特定应用的图像数据进行调查,以提取具有启发性的信息并达到明确而清晰的结果。图像处理中最重要的阶段之一是特征提取,它是继图像采集和分割之后的第三步。将输入图像重构成一组特征的过程称为特征提取。这些特征构成了图像的纹理特征。纹理特征提取是特征提取的重要组成部分,对分类结果的影响很大。纹理主要基于对图像中感兴趣的物体或区域的识别。局部二元模式特征描述符将是本文讨论的重点。LBP是一种纹理算子,它通过对每个像素的邻域阈值进行标记来对图像进行操作。为了深入了解使用LBP进行模式识别的趋势,本文引用了各种高质量的期刊。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Local Binary Pattern Based texture feature extraction
In the sphere of image processing, image data investigation is required related to a specific application in order to extract the suggestive information and reach defined and crisp culminations. One of the most significant phase in image processing is feature extraction which is the third step following image acquisition and segmentation. The procedure of reconstructing the input image into a group of features is named as feature extraction. These features construe the textural characteristics of the image. Texture feature extraction is one such significant part of feature extraction that on majority influences the results of classification. A texture is principally based on recognizing the object or region of interest in an image. The Local Binary Pattern feature descriptor will be the pith of discussion of this paper. LBP is a texture operator that operates on an image by labeling its pixels by thresholding neighborhood of each pixel. Various quality journals have been referred in order to provide an insight into the trends in pattern recognition using LBP.
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