Pattern recognition using 3-D moments

A 3-D moment method of object identification and positioning is proposed. Moments are computed from 3-D CAT image functions, 2.5-D range data, space curves, and discrete 3-D points. Objects are recognized by their shapes via moment invariants. Using an algebraic method, scalars and vectors are extracted from a compound of moments using Clebsch-Gordon expansion. The vectors are used to estimate position parameters of the object. Moment features of range data can be used in view-independent object recognition when the three-layer perceptron encodes the feature space distribution of the object in the weights of the network. Objects are recognized from an arbitrary viewpoint by the trained network.<>