We have described how vectors of image invariants can be used to locate matches between one image and another, and how we can use a probabilistic measure of distinctiveness to determine those points least likely to generate false positive matches.
A single match constrains the position and scale of the object in the second image, but not its orientation. By using the matches from several distinct points we anticipate that we can achieve a robust correspondence between objects in two images. Cootes et al [ 4 ] demonstrated how statistical models of the relative positions of the points could be used to select the most plausible set of responses. We intend to use this approach with our feature detectors.
So far we have only considered locating distinctive points in a single image, which means there is no guarantee that similar points exist in similar images. We intend to generalise the algorithm to work on sets of images, discarding distinctive points which are not present in the majority.
We anticipate that the method of detecting distinctive points and of locating their positions in new images will prove useful both for generating cues to prime a shape model for search, and to help to automatically train a statistical shape model by locating common landmarks on a set of training images.
Figure:
Scale image calculated corresponding to the distinctness image in Figure
6
. Dark regions indicate fine scales and light regions indicate coarse
scales.
Kevin Walker