摘要
快速l1最小化算法是一种关于求解稀疏矩阵的算法,相对于传统的主成分分析l2范数,l1范数只需要计算图像主要特征的稀疏矩阵,对噪声和异常项具有更好的鲁棒性,且在木材识别领域使用较少。局部二元模式(Local binary pattern,LBP)是一种描述灰度范围纹理的算法,对于图像特征的描述有显著的效果。本文利用LBP提取不同木材截面RGB图像三层纹理的特征,用l1算法对特征矩阵进行快速、准确的匹配,检测出是否有缺陷,同时通过图像分块定位缺陷的位置坐标。实验表明快速l1算法结合LBP算子对木材缺陷定位正确率达到0.931。
l1-minimization algorithm is one of the hot topics in the signal processing and optimization communities in solving the sparsest matrix. Compared with the traditional principal component analysis using 12 norm, the l1 norm only calculates the main characteristics matrix of the image, which is more robust to noise and abnormal data. While it is used too few in wood identification. The local binary pattern （LBP） texture analysis operator is defined as a gray-scale invariant texture measure. LBP algorithm is important in view point of pattern classification, and can be used to extract three-layer cross-sectional features of different wood RGB images data. And then a fast l1 norm algorithm is used to implement fast and accurate identification to judge whether the wood surface has defects or not, and where defects locate. Many experiments indicte that fast l1 algorithm combined with LBP can get correction of 0. 931 for defect location in wood surface.
出处
《数据采集与处理》
CSCD
北大核心
2017年第6期1223-1231,共9页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(31300472)资助项目
江苏省自然科学基金(BK2012418)资助项目
农业部儋州热带作物科学观测实验站开放课题基金(RRI-KLOF201501)资助项目
国家重大基础研究发展计划("九七三"计划)(2012CB416904)资助项目
江苏省高校优势学科建设工程资助项目
关键词
快速l1最小化算法
局部二值模式
识别算法
木材缺陷
l1-minimization algorithm
local binary pattern （LBP）
identify algorithm
wood defects
