BMVC 2004, Kingston, 7th-9th Sept, 2004
Extending Pictorial Structures for Object Recognition
M. Pawan Kumar, P.H.S. Torr (Oxford Brookes University) and A.
Zisserman (University of Oxford)
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.