Well you get a collection of relatively uniform kittens faces say, then you have the computer approximate where certain kitten-like features are, then you create orthogonal representations of the variances between features, in descending order so that your first n vectors describe the most variation in your sample, then you go about scanning a (grayscale and rotated) version of the new image, checking the distance in the feature-space (nearer to 0 the more likely it is a positive match for that feature), sum up all the distances for all the features in the different scans of the images, and if one of those numbers is greater than some arbitrary number k, it's a kittenOf course, my other friend thinks a simple drag-and-drop would work well. "Drag the round peg into the round hole."
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posted by AuntLisa at 2:07 PM on April 7, 2006