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Recognition of Partially Occluded Objects with Back-Propagation Neural Network

The problem of occlusion in a two-dimensional scene introduces errors into many existing vision algorithms that cannot be resolved. Occlusion occurs where two or more objects in a given image touch or overlap one another. Since occlusion will be present in all but the most constrained environment, the recognition of partially occluded objects is important for industrial machine vision applications to solve real problems in the military domain and in factory automation. A new method is proposed in this paper to identify and locate objects lying on a flat surface. The method is based on a local and compact description of the objects' boundaries and a new fast recognition method involving neural networks. The merit of such approach is that it provides strong robustness for partially occluded object recognition. The method is integrated into a vision system that couples with an industrial robot arm to provide automatic picking and repositioning of partially occluded industrial parts.