Sparse representation shape model

This paper introduces a novel shape model, Sparse Representation Shape Model (SRSM). Rather than for modeling specific deformable shapes, this model is specially designed for shape segmentation and matching. This model is utilized under the framework of Active Shape Models (ASM). Unlike the Linear Point Distribution Model utilized by original ASM, which relies on obscure statistical boundary to do shape regularization, SRSM distinctly distinguishes valid shape information and errors contained in input candidate shape from structure and by making use of sparse representation, SRSM could acquire the maximum valid shape information, and hence could achieve optimal shape regularization. Further-more, through exploiting the reliability information of each landmark, SRSM can be improved further to form Weighted SRSM, which is much more evident and accurate.