BMVC 2004, Kingston, 7th-9th Sept, 2004

Likelihood Models For Template Matching using the PDF Projection Theorem
A. Thayananthan, R. Navaratnam (University of Cambridge) P. H. S. Torr
(Oxford Brookes University) and R. Cipolla (University of Cambridge)

Template matching techniques are widely used in many computer vision
tasks. Generally, a likelihood value is calculated from similarity measures,
however the relation between these measures and the data likelihood is often
incorrectly stated. It is clear that accurate likelihood estimation will improve
the efficiency of the matching algorithms. This paper introduces a novel
method for estimating the likelihood PDFS accurately based on the PDF
Projection Theorem, which provides the correct relation between the feature
likelihood and the data likelihood, permitting the use of different types of
features for different types of objects and still estimating consistent likelihoods.
The proposed method removes the normalization and bias problems
that are usually associated with the likelihood calculations. We demonstrate
that it significantly improves template matching in pose estimation problems.
Qualitative and quantitative results are compared against traditional likelihood
estimation schemes.
(pdf article)