CLUSTER-BASED NONLINEAR PRINCIPLE COMPONENT ANALYSIS

In the field of computer vision, principle component analysis (PCA) is often used to provide statistical models of shape, deformation or app earance. This simple statistical model provides a constrained. compact approach to model based vision. However, as larger problems are consi dered. high dimensionality and nonlinearity make linear PCA an unsuita ble and unreliable approach. A nonlinear PCA (NLPCA) technique is prop osed which uses cluster analysis and dimensional reduction to provide a fast. robust solution. Simulation results on both 2D contour models and greyscale images are presented.