Cool. But finding the distance between two N-D points isn't really hard at all. What is hard is finding the distance between a point and a set of points. Or a

Cool. But finding the distance between two N-D points isn't really hard at all. What is hard is finding the distance between a point and a set of points. Or a set and a set. Doing exhaustive search is wasteful. If you're interested, look in to K-D trees as a real solution. Best-bin-first modified K-D trees are the basis of the SIFT object instance recognition feature matching algorithm. Break an image into a set of ND features. Matching a geometrically consistent subset of those features to a previously seen object works extremely well. The algorithm is general. Change and add features to make it more powerful or faster. But the idea of using a set of ND linear features to describe an object should last for years.