Three-dimensional object recognition using x-ray imaging

This paper presents a distortion-tolerant 3-D volume object recognition technique. Volumetric information on 3-D objects is recon- structed by x-ray imaging. We introduce 3-D feature extraction, volume matching, and statistical significance testing for the 3-D object recogni- tion. The 3D Gabor-based wavelets extract salient features from 3-D volume objects and represent them in the 3-D spatial-frequency domain. Gabor coefficients constitute feature vectors that are invariant to trans- lation, rotation, and distortion. Distortion-tolerant volume matching is per- formed by a modified 3-D dynamic link association (DLA). The DLA is composed of two stages: rigid motion of a 3-D graph, and elastic defor- mation of the graph. Our 3-D DLA presents a simple and straightforward solution for a 3-D volume matching task. Finally, significance testing de- cides the class of input objects in a statistical manner. Experiment and simulation results are presented for five classes of volume objects. We test three classes of synthetic data (pyramid, hemisphere, and cone) and two classes of experimental data (short screw and long screw). The recognition performance is analyzed in terms of the mean absolute error between references and input volume objects. We also confirm the ro- bustness of the recognition algorithm by varying system parameters. © 2005 Society of Photo-Optical Instrumentation Engineers. (DOI: 10.1117/1.1844532) Subject terms: 3-D image processing; image reconstruction; image recognition; image classification; x-ray imaging systems.

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