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.
The multithresholding criterion is derived from maximizing the interclass variance and hence the average of the c.g. (center of gravity) of two neighboring class pixel values should be equal to the interclass threshold value. The learning (weight matrix evolution) procedure of the neural model is developed based on the above condition. We use a three-layer neural network with binary weight synapses. The number of neurons in the first layer equals to that of the level of the range image and complex number inputs are used because the arguments of second layer outputs represent the c.g. of the class. The third layer neurons receive the argument output of the second layer and give an indication of the reach of the optimum condition.
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