FPGA architecture for fast parallel computation of co-occurrence matrices

This paper presents a novel architecture for fast parallel computation of co-occurrence matrices in high throughput image analysis applications for which time performance is critical. The architecture was implemented on a Xilinx Virtex-XCV2000E-6 FPGA using VHDL. The symmetry and sparseness of the co-occurrence matrices are exploited to achieve improved processing times, and smaller, flexible area utilization as compared with the state of the art. The performance of the proposed architecture is evaluated using input images of various dimensions, in comparison with an optimized software implementation running on a conventional general purpose processor. Simulations of the architecture on contemporary FPGA devices show that it can deliver a speedup of two orders of magnitude over software.