M. Pawan Kumar
 
 

HOME

RESEARCH

PUBLICATIONS

GROUP

TALKS

TEACHING

CV

 

 

 

 

 

 

EXTENDING PICTORIAL STRUCTURES FOR OBJECT RECOGNITION

M. Pawan Kumar, P. Torr and A. Zisserman

In Proceedings of British Machine Vision Conference (BMVC), 2004

The goal of this paper is to recognize various deformable objects from images. To this end we extend the class of generative probabilistic models known as pictorial structures. This class of models is particularly suited to represent articulated structures, and has previously been used by Felzenszwalb and Huttenlocher for pose estimation of humans. We extend pictorial structures in three ways: (i) likelihoods are included for both the boundary and the enclosed texture of the animal; (ii) a complete graph is modelled (rather than a tree structure); (iii) it is demonstrated that the model can be fitted in polynomial time using belief propagation. We show examples for two types of quadrupeds, cows and horses. We achieve excellent recognition performance for cows with an equal error rate of 3% for 500 positive and 5000 negative images.

[Paper]  [Poster]