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Identification of three-dimensional objects using range information
April 1989 (vol. 11 no. 4)
pp. 403,404,405,406,407,408,409,410
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:
computerised picture processing,computerised pattern recognition,polyhedral approximation,3D objects identification,pattern recognition,computerised picture processing,range data,model-based feature vector classification,Fourier descriptors,contour imagery,silhouette,range imagery,Testing,Object recognition,Libraries,Image sampling,Airplanes,Spatial resolution,Image resolution,Image classification,Noise reduction,Noise level
"Identification of three-dimensional objects using range information," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 4, pp. 403,404,405,406,407,408,409,410, April 1989, doi:10.1109/34.19036
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