Skip to Main Content
Kernel principal component analysis (PCA) is proposed as a nonlinear technique for dimensionality reduction. The basic idea is to map the input space into a feature space via nonlinear mapping and then compute the principal component in the feature space. In this paper, we utilize kernel PCA technique into 3D object recognition and pose estimation, and present results of appearance-based object recognition accomplished by employing a neural network architecture on the base of kernel PCA. Through adopting a polynomial kernel, the principal component can be computed in the space spanned by high-order correlations of input pixels. We illustrate the potential of kernel PCA on a database of 1,440 images of 20 different objects. The excellent recognition rates achieved in all of the performed experiments indicate that the proposed method is well-suited for object recognition and pose estimation.