Identification of Three-Dimensional Objects Using Range Information
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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.
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Index Terms:
3D objects identification; pattern recognition; computerised picture processing; range data; model-based feature vector classification; Fourier descriptors; contour imagery; silhouette; range imagery; polyhedral approximation; computerised pattern recognition; computerised picture processing
Citation:
A.P. Reeves, R.W. Quong, "Identification of Three-Dimensional Objects Using Range Information," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 4, pp. 403-410, Apr. 1989, doi:10.1109/34.19036
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