Simultaneous variational image segmentation and object recognition via shape sparse representation
Fei Chen, Huimin Yu, Roland Hu · 2010
In this paper, we propose a novel model for simultaneous image segmentation and object recognition. Our model is different from previous prior-based level set variatioinal image segmentation in two aspects. The first is the use of the shape sparse representation, which is able to integrate shape priors by linear combination into variational image segmentation. The second is that segmentation and recognition procedures are carried out automatically. The sparsest solution will determine the identity of the target. In addition, our model can handle more general shape priors. Numerical experiments show promising results on synthetic and real images.