Kernel PCA and Nonlinear ASM

摘要

As a nonlinear Principal Component Analysis (PCA) method, Kernel PCA (KPCA) can effectively extract nonlinear feature. For the object image which includes more nonlinear features, traditional Active Shape Model (ASM) couldn't obtain a good result of localization. Concerning this, an extending research on nonlinear-ASM is brought here, and an algorithm of object localization based on nonlinear-ASM is proposed. In the research of nonlinear-ASM, the problem of high dimensionality caused by nonlinear mapping has been solved effectively by the kernel theory. Besides, KPCA can not reconstruct the pre-image of the input space, thus prior model is hardly constructed by the method of the nonlinear-ASM. For solving this problem, the theory of multi-dimensional scaling is researched in the paper. The validity of the proposed method is demonstrated by the results of experiments.