We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as ...We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a elas- sifter, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and se- lected eight principal components to express defects. After the fusion process, we used the features to construct a data dic- tionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms, respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %.展开更多
针对压缩感知(Compressed Sensing,CS)中信号重构的l1-正则化问题中的l1-正则项非光滑,求解比较困难,提出了交替方向外点持续法(Alternating Direction Exterior Point Continuation Method,ADEPCM).该算法首先将信号的稀疏域的l1-正则...针对压缩感知(Compressed Sensing,CS)中信号重构的l1-正则化问题中的l1-正则项非光滑,求解比较困难,提出了交替方向外点持续法(Alternating Direction Exterior Point Continuation Method,ADEPCM).该算法首先将信号的稀疏域的l1-正则化问题通过变量分裂(Variable Splitting,VS)技术转化为与之等价的约束优化问题;然后采用一步Gauss-Seidel思想,对优化问题中的变量最小化,并采用持续的思想更新罚参数,重构出信号的稀疏系数;最后进行正交反变换,重构出原始信号.并将ADEPCM用于图像重构,进行了仿真实验及对实验结果进行了分析.实验结果表明:与现有的一些重构算法相比,ADEPCM具有稍高的峰值信噪比(Peak Signal to Noise Ratio,PSNR)和更快速的收敛速度.展开更多
基金financially supported by the Fund of Forestry 948 Project(2011-4-04)the Fundamental Research Funds for the Central Universities(DL13CB02,DL13BB21)the Natural Science Foundation of Heilongjiang Province(C201415)
文摘We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a elas- sifter, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and se- lected eight principal components to express defects. After the fusion process, we used the features to construct a data dic- tionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms, respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %.
文摘针对压缩感知(Compressed Sensing,CS)中信号重构的l1-正则化问题中的l1-正则项非光滑,求解比较困难,提出了交替方向外点持续法(Alternating Direction Exterior Point Continuation Method,ADEPCM).该算法首先将信号的稀疏域的l1-正则化问题通过变量分裂(Variable Splitting,VS)技术转化为与之等价的约束优化问题;然后采用一步Gauss-Seidel思想,对优化问题中的变量最小化,并采用持续的思想更新罚参数,重构出信号的稀疏系数;最后进行正交反变换,重构出原始信号.并将ADEPCM用于图像重构,进行了仿真实验及对实验结果进行了分析.实验结果表明:与现有的一些重构算法相比,ADEPCM具有稍高的峰值信噪比(Peak Signal to Noise Ratio,PSNR)和更快速的收敛速度.
