主动形状模型中搜索过程与搜索空间的改进

Improvements on Search Process and Search Subspace in Active Shape Model

主动形状模型是近年来广泛使用的特征点对准算法.但是其搜索空间难以重构出现实中复杂多变的搜索对象.另外,搜索过程中由于没有提示或约束,故所得结果不稳定.因此,本文改进搜索空间与搜索过程,通过在搜索空间中加入主要形状变化子空间,重构模型更加泛化,利用搜索过程中的误差来分析搜索状态,反馈约束下一步的搜索方向和过程.这样的反馈、迭代搜索过程大大增加搜索过程的主动性和目的性,且搜索结果唯一.最终的人脸对准实验证明,本文的改进较大地提高了对准精度.

The method of active shape model （ASM） is quite commonly used for alignment in recent years. However, its search subspace has a number of limitations on reconstructing the changeable shape in real life. In addition, since there is no restriction during searching, the result is unstable. In this paper, some improvements on search subspace and search process are proposed. By adding eigen-shape variance information to search subspace, the new search subspace can reconstruct the shape more generally. In the meantime, the search errors are analyzed and searching information of the next step is obtained. The whole search processing is iterated and connected with the feedback of search error. The feedback and iteration make the search processing more active and the final search result is unique. Several face alignment experiments are designed and the results show that the proposed method improves alignment precision greatly.