  Recognising and locating image patches or sets of image features is an
important task underlying much work in computer vision. Traditionally this has
been accomplished using template matching. However, template matching is
notoriously brittle in the face of changes in appearance caused by, for
example, variations in viewpoint, partial occlusion, and non-rigid
deformations. This article tests a method of template matching that is more
tolerant to such changes in appearance and that can, therefore, more accurately
identify image patches. In traditional template matching the comparison between
a template and the image is independent of the other templates. In contrast,
the method advocated here takes into account the evidence provided by the image
for the template at each location and the full range of alternative
explanations represented by the same template at other locations and by other
templates. Specifically, the proposed method of template matching is performed
using a form of probabilistic inference known as "explaining away". The
algorithm used to implement explaining away has previously been used to
simulate several neurobiological mechanisms, and been applied to image contour
detection and pattern recognition tasks. Here it is applied for the first time
to image patch matching, and is shown to produce superior results in comparison
to the current state-of-the-art methods.
