.png){kind=icon}
返回
Two novel local binary pattern descriptors for texture analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2015.06.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The recent developments in the image quality, storage and data transmission capabilities increase the importance of texture analysis, which plays an important role in computer vision and image processing. Local binary pattern (LBP) is an effective statistical texture descriptor, which has successful applications in texture classification. In this paper, two novel descriptors were proposed to search different patterns in images built on LBP. One of them is based on the relations between the sequential neighbors with a specified distance and the other one is based on determining the neighbors in the same orientation through central pixel parameter. These descriptors are tested with the Brodatz-1, Brodatz-2, Butterfly and Kylberg datasets to show the applicability of the proposed nLBP(d) and dLBP(alpha) descriptors. The proposed methods are also compared with classical LBP. The average accuracies obtained by ANN with 10 fold cross validation, which are 99.26% (LBPu2 and nLBP(d)), 94.44% (dLBP(alpha)), 95.71% (nLBP(d)(u2)) and %99.64 (nLBP(d)), for Brodatz-1, Brodatz-2, Butterfly and Kylberg datasets, respectively, show that the proposed methods outperform significant accuracies. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Local binary patterns
nLBP(d)
dLBP(alpha)
Texture classification
Feature extraction
Image classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
分享收藏Nanocrystalline silicon films as multifunctional material for optoelectronic and photovoltaic applications纳米硅薄膜作为光电和光伏应用的多功能材料
PREAI
分享收藏Noise tolerant local binary pattern operator for efficient texture analysis用于有效纹理分析的噪声容忍局部二进制模式算子
PREAI
分享收藏
查看更多内容