Stereoscopic neuro-vision for three-dimensional object recognition

Neural network technology has provided new methodologies for solving difficult computational problems in many areas of science and engineering. Neural networks, along with their varied learning techniques, have replaced complicated mathematical models, complex estimation techniques, or optimization procedures in several applications. One particular area seeing much benefit from these new computational paradigms is machine vision. The machine vision field has long needed approaches offering robust operation, massive parallel and distributed computational capabilities, and graceful system degradation. Neural networks offer these capabilities along with the potential of direct hardware implementation. rticle demonstrates several novel uses of artificial neural networks in the processing of stereoscopic images for three-dimensional object recognition. It will be shown that several different types of neural networks can be combined, with a rule base and conventional processing techniques, for the creation of a powerful 3-D object recognition system. This hybrid system has been tested on several simple objects and the results are presented.