A linear systolic array for real-time morphological image processing

Mathematical morphology has proven to be a very useful tool for applications such as smoothing, image skeletonization, pattern recognition, machine vision, etc. In this paper we present a 1-dimensional systolic architecture for the basic gray-scale morphology operations: dilation and erosion. Most other morphological operations like opening and closing, are also supported by the architecture since these operations are combinations of the basic ones. The advantages of our design stem from the fact that it has pipeline period alpha = 1 (i.e., 100% processor utilization), it requires simple communications, and it is exploiting the simplicity of the morphological operations to make it possible to implement them in a linear target machine although the starting algorithm is a generalized 2-D convolution. We also propose a Locally Parallel Globally Sequential (LPGS) partitioning strategy for the best mapping of the algorithm onto the architecture. We conclude that for this particular problem LPGS is better than LSGP in a practical sense (pinout, memory requirement, etc.). Furthermore, we propose a chip design for the basic component of the array that will allow real-time video processing for 8- and 16-bit gray-level frames of size 512 x 512, using only 32 processors in parallel. The design is easily scalable so it can be custom-taylored to fit the requirement of each particular application.