Machine recognition of partial planar shapes using feature vectors

A new concept for examining shapes as vectors in a shape space is described. The shape space is defined in terms of its properties and the importance of the independence of the size variable to the shape vectors defined on this shape space is stressed. Also, two theorems essential to the process of comparing partial shapes to the complete shape are given. A new method for detecting the points on a shape which appear to dominate visual perception is described. This method, called the Adaptive Line of Sight Method detects the dominant points of high curvature. The critical points, or the dominant points of the shape, which are determined by this method are based on a set of axes that are dependent on the shape itself. Therefore, the points determined are independent of size, rotation, or relative displacement. This concept is utilized to extract features from a shape. It is demonstrated that the feature vectors determined by this procedure are independent of size rotation, and displacement.