Abstract: This paper describes a computationally efficient 3D object surface matching algorithm. In the proposed method, object and model surfaces are scaled to be in a unit cube in the 3D space. They are then sliced along the magnitude axis and the resultant object and model surface cross sections are represented in binary image format. The cross- sections' centroids of an unknown object and the models of different shapes are computed in their respective binary images. The resultant cross-sections are translated to the origin of the spatial plane using the centroids. Major and minor axes of the plane cross sections are aligned with the coordinate axes of the spatial plane. Matching of the aligned cross sections is done in the direction of the gradient of the cross section boundary by computing the shape deformation as the Euclidean distance between the object boundary points and the corresponding points in the model cross section boundary. The shape deformation distances measured in different cross sections are average and the minimum average shape deformation distance is used to identify the model best matching to the object of unknown classification.!19
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