Abstract
This paper proposes an effective algorithm for 3D object recognition in range images using hidden Markov models (HMMs) and neural networks (NNs). Because an HMM trains each object independently, it is adequate to recognition applications in which a modelbase is frequently updated. Also NNs are employed as a postprocessing step to increase the recognition rate. To avoid the dependency of feature values on the viewing position, we employ 3D features such as surface type, moments, surface area, line length, and relationship between adjacent surfaces. In representing feature values, a fuzzy membership function is used to absorb variation of feature values caused by occlusion of the 3D object. Computer simulation results for synthetic and real range images show that the proposed HMM-based system combined with NNs can be successfully applied to recognition of 3D objects.
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