3D object recognition using segment-based stereo vision
We propose a new method to recognize 3D objects using segment-based stereo vision. Predefined object models are compared with 3D boundaries which are extracted by the stereo vision. Boundaries may be straight lines, circular arcs and free-form curves. The models consist of the local shapes and the whole shapes of the boundaries. The models are constructed from samples of real objects or from CAD models. Based on the local shapes, the candidate transformations are generated. The candidates are verified and adjusted based on the whole shapes. Experimental results show the effectiveness of the method.
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