Simultaneous target recognition, segmentation and pose estimation
Liangjiang Yu, Guoliang Fan, Jiulu Gong, Joseph Havlicek · 2013
We propose a simultaneous target recognition, segmentation and pose estimation algorithm for the infrared ATR task. A probabilistic framework of level set segmentation is extended by incorporating a shape generative model that provides a multi-class and multiview shape prior. This generative model involves a couplet of a view manifold and an identity manifold for general shape modeling. Then an energy function from the probabilistic level set formulation can be iteratively optimized by a shape-constrained variational method. Due to the fact that both the view and identity variables are explicitly involved in the level set optimization, the proposed method is able to accomplish recognition, segmentation, and pose estimation. Experimental results show that the proposed method outperforms two traditional methods where target recognition and pose estimation are implemented after segmentation.