Segmentation of Range Images: A Neural Network Approach

In this paper we present a neural computation model for histogram based range image segmentation. An optimal thresholding vector for the range histogram is determined. The number of elements in the vector is characterized by the histogram. Since our model is the parallel implementation of maximum interclass variance thresholding, the time for convergence will be much faster. Together with a real-time histogram builder, real time adaptive range image segmentation can be achieved.

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