介绍了由V. Zarzoso and P. Comon提出的一种新的基于峭度的独立成分分析算法RobustICA(Robust Independent Component Analysis),并比较了和FastICA在收敛性和信号质量方面的不同。该算法的主要优点在于可以选取最佳步长,可以选取任何...介绍了由V. Zarzoso and P. Comon提出的一种新的基于峭度的独立成分分析算法RobustICA(Robust Independent Component Analysis),并比较了和FastICA在收敛性和信号质量方面的不同。该算法的主要优点在于可以选取最佳步长,可以选取任何不为零的独立成分,并且解决盲分离信号排序问题,同时提升当信号存在坏点和伪局部极值点时的鲁棒性。仿真实验结果表明了该算法相对于FastICA算法减少了迭代次数和加快了收敛速度,同时在小样本空间下均方误差SMSE也明显优于FastICA算法。展开更多
This paper proposes the efficient model building in active appearance model(AAM) for the rotated face.Finding an exact region of the face is generally difficult due to different shapes and viewpoints.Unlike many paper...This paper proposes the efficient model building in active appearance model(AAM) for the rotated face.Finding an exact region of the face is generally difficult due to different shapes and viewpoints.Unlike many papers about the fitting method of AAM,this paper treats how images are chosen for fitting of the rotated face in modelling process.To solve this problem,databases of facial rotation and expression are selected and models are built using Procrustes method and principal component analysis(PCA).These models are applied in fitting methods like basic AAM fitting,inverse compositional alignment(ICA),project-out ICA,normalization ICA,robust normalization inverse compositional algorithm(RNIC)and efficient robust normalization algorithm(ERN).RNIC and ERN can fit the rotated face in images efficiently.The efficiency of model building is checked using sequence images made by ourselves.展开更多
文摘介绍了由V. Zarzoso and P. Comon提出的一种新的基于峭度的独立成分分析算法RobustICA(Robust Independent Component Analysis),并比较了和FastICA在收敛性和信号质量方面的不同。该算法的主要优点在于可以选取最佳步长,可以选取任何不为零的独立成分,并且解决盲分离信号排序问题,同时提升当信号存在坏点和伪局部极值点时的鲁棒性。仿真实验结果表明了该算法相对于FastICA算法减少了迭代次数和加快了收敛速度,同时在小样本空间下均方误差SMSE也明显优于FastICA算法。
基金Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(No.2012M3C4A7032182)The MSIP(Ministry of Science,ICT&Future Planning),Korea,under the ITRC(Information Technology Research Center)support program(NIPA-2013-H0301-13-2006)supervised by the NIPA(National IT Industry Promotion Agency)
文摘This paper proposes the efficient model building in active appearance model(AAM) for the rotated face.Finding an exact region of the face is generally difficult due to different shapes and viewpoints.Unlike many papers about the fitting method of AAM,this paper treats how images are chosen for fitting of the rotated face in modelling process.To solve this problem,databases of facial rotation and expression are selected and models are built using Procrustes method and principal component analysis(PCA).These models are applied in fitting methods like basic AAM fitting,inverse compositional alignment(ICA),project-out ICA,normalization ICA,robust normalization inverse compositional algorithm(RNIC)and efficient robust normalization algorithm(ERN).RNIC and ERN can fit the rotated face in images efficiently.The efficiency of model building is checked using sequence images made by ourselves.
