A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation

We introduce both shape prior and edge information to Markov random field (MRF) to segment target of interest in images. Kernel Principal component analysis (PCA) is performed on a set of training shapes to obtain statistical shape representation. Edges are extracted directly from images. Both of them are added to the MRF energy function and the integrated energy function is minimized by graph cuts. An alignment procedure is presented to deal with variations between the target object and shape templates. Edge information makes the influence of inaccurate shape alignment not too severe, and brings result smoother. The experiments indicate that shape and edge play important roles for complete and robust foreground segmentation.