A common problem in computer vision is that of establishing correspondences between images of similar objects. For images of objects with fixed 3D geometry 5 correspondences are, in principle, sufficient. For more variable classes of object, such as faces, a much larger number of correspondences may be required. Previous approaches have tended to rely on matching image features chosen by the system designer [ 4 ][ 2 ]. We have explored the idea of automatically selecting features (and their scales) which are unlikely to be confused with other points.
The approach uses the differential structure of the image to construct a vector of invariants over a range of scales at each image point. These vectors describe the local geometry around a point at a particular scale independently of translation and orientation.
In order to locate distinctive points we estimate the probability density distribution of the vectors of invariants representing candidate points over a range of scales, and choose those points whose vectors lie in low probability regions.
The vector of invariants describing a distinct point in one image can then be used to search for similar points in a second image by comparing the distinct point's vector with each of the vectors extracted from all points and scales in the second image.
In the following we describe how to construct the vectors of invariants, how to determine distinctive vectors and show how they can be used to locate corresponding features in a new image.
Kevin Walker