|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
LIKELIHOOD MODELS FOR TEMPLATE MATCHING USING PDF PROJECTION THEOREM
A. Thayananthan, R.Navaratnam, P. H. S. Torr, and R. Cipolla
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 ambiguous. It is clear that accurate likelihood estimation will improve the efficiency of the matching algorithms, reducing the number of false positives and increasing the convergence rates. This paper introduces a novel method for estimating the likelihood PDFS accurately based on the PDF Projection Theorem, which derives the correct relation between the feature likelihood and the data likelihood. Furthermore, its formulation permits the use of different types of features for different types of objects and still estimates 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.
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