基于KPCA的非线性ASM定位方法研究

Object Localization of Nonlinear Active Shape Model Based on Kernel PCA

针对主动形状模型(Active Shape Model,ASM)中的建模方法——主分量分析(Principal Component Anal-ysis,PCA)存在容易忽略像素间非线性关系等弱点,对基于ASM的图像中目标物体定位技术进行了非线性化的研究探索.通过引入核理论,提出了一种基于核主分量分析(Kernel PCA,KPCA)的非线性ASM定位方法,并运用多维空间距离关系原理解决了KPCA方法无法重构原空间图像模型的难题.实验证明,此方法能够有效地实现二维图像中的目标物体定位.

In this paper,we address the problem of principal component analysis technique which is the modeling method of active shape model.Since the principal component analysis usually ignores nonlinear relativity among a large number of pixels,kernel method is combined to research object localization.A nonlinear active shape model based on kernel principal component analysis is presented in this paper.The proposed method reconstructs the pre-image in input space through the idea in multidimensional scaling.The validity of this method is demonstrated by the results of preliminary experiments.