Efficient binary and grey level morphological operations on a massively parallel processor

One of the most important features in image analysis and understanding is shape. Mathematical morphology is the image processing branch that deals with shape analysis. The definition of all morphological transformations is based on two primitive operations, i.e. dilation and erosion. Since many applications require the solution of morphological problems in real time, researching time efficient algorithms for these two operations is crucial. †The implementation of the above functions is beyond the scope of this paper. In this paper, efficient algorithms for the binary as well as the grey level dilation and erosion are presented and evaluated for an advanced associative processor. It is shown through simulation results that the above architecture is near optimal in the binary case and is also as efficient as the array processor with a 2D-mesh interconnection in the grey level case. Finally, it is proven that the implementation of this image processing machine is economically feasible.