Artificial neural networks and model-based recognition of three-dimensional objects from two-dimensional images

A computer vision system is developed for 3-D object recognition using artificial neural networks and a model-based top-down feedback analysis approach. This system can adequately address the problems caused by an incomplete edge map provided by a low-level processor for 3-D representation and recognition. The system uses key patterns that are selected using a priority assignment. The highest priority is given to the key pattern with the most connected node and associated features. The features are space invariant structures and sets of orientation for edge primitives. The labeled key features are provided as input to an artificial neural network for matching with model key patterns. A Hopfield-Tank network is applied to two levels of matching to increase the computational effectiveness. The first matching is to choose the class of the possible model and the second matching is to find the model closest to the candidate. The result of such matchings is utilized in generating the model-driven top-down feedback analysis. This model is then rotated in 3-D space to find the best match with the candidate and to provide the additional features in 3-D. In the case of multiple objects, a dynamic search strategy is adopted to recognize objects using one pattern at a time. This strategy is also useful in recognizing occluded objects. The experimental results are presented to show the capability and effectiveness of the system.