Hyperspectral feature recognition based on kernel PCA and relational perspective map

A novel joint kernel principal component analysis （PCA） and relational perspective map （RPM） method called KPmapper is proposed for hyperspectral dimensionality reduction and spectral feature recognition. Kernel PCA is used to analyze hyperspectral data so that the major information corresponding to features can be better extracted. RPM is used to visualize hyperspectral data through two-dimensional （2D） maps, and it is an efficient approach to discover regularities and extract information by partitioning the data into pieces and mapping them onto a 2D space. The experimental results prove that the KPmapper algorithm can effectively obtain the intrinsic features in nonlinear high dimensional data. It is useful and impressing for dimensionality reduction and spectral feature recognition.     

光电混合实时指纹识别新方法

在实验上建立了一套基于联合变换相关的１ｆ光电混合实时指纹识别系统，用棱镜和ＣＣＤ摄像机结合实现指纹的实时采集，提出了用二次微分边缘提取算法对指纹进行预处理，用监视器上亮度和对比度调节来实现联合变换功率谱的视觉二值化。