Identification of three-dimensional objects using range information

A method for identifying unoccluded three-dimensional objects from arbitrary viewing angles is presented. The technique uses synthetically generated range data in a model-based feature vector classification scheme. Fourier descriptors and moments are used for feature vector generation from, respectively, contour imagery, and silhouette or range imagery. A method is developed for generating an exhaustive set of library views and worst-case test views that is based on a polyhedral approximation to a sphere. Analysis of the success of this approach is made with experiments on a six-airplane data set. A model of range data noise is developed, and results are presented for both ideal and noisy lower-resolution image-classification tests. The use of multiple views for object identification is discussed, and results for one-, two-, and three-view tests are presented.<>