Optimal shape space and searching in the active shape model

摘要

A novel idea, called the optimal shape subspace (OSS) is first proposed for optimizing active shape model (ASM) search. It is constructed from the principal shape subspace and the principal shape variance subspace. It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space, hence it is more expressive in representing shapes in real life. Then a cost function is developed, based on a study on the search process. An optimal searching method using the feedback information provided by the evaluation cost is proposed to improve the performance of ASM alignment. Experimental results show that the proposed OSS can offer the maximum shape variation with reserving the principal information and a unique local optimal shape is acquired after optimal searching. The combination of OSS and optimal searching can improve the ASM performance greatly.