Many people have studied the problem of finding point correspondences between images using vectors of local image descriptors. Lades et al [ 8 ] used a vectors containing the responses of Gabor wavelets at different wavelengths to measure local image structure. An object was then represented by a set of these vectors extracted at the vertices from a sparse grid placed over the object of interest. The vertices of the grid are unlikely to fall over the most distinctive points, so when searching for the grid in a similar image only the vertices which happened to lie near distinctive points are found accurately. Triesch et al [ 13 ] tackles this problem by extracting the vectors from the vertices of a graph which is placed manually over the object. The vertices are chosen to coincide with heavily textured positions (ie distinctive points). Our approach improves on this by providing an automatic, principled way of locating these points.
Several groups have used statistical models of the intensities in a patch ('eigen features') to locate features [ 4 ][ 10 ].
Many authors have shown that using the distinctiveness of image features can improve the robustness in object recognition algorithms [ 1 ] [ 14 ] [ 11 ], but this typically been applied to finding distinctive segments on an object boundary.
Florack et al [ 6 ] describes how cartesian differential invariants can be constructed from Gaussian differential operators which describe the local image geometry independently of spatial position and orientation.
Kevin Walker