2003•IEEE Transactions on Very Large Scale Integration (VLSI) SystemsRequires access

Mapping of generalized template matching onto reconfigurable computers

Xuejun Liang, Jack Jean

Open publisher page 24 citations

Abstract

Image processing algorithms for template matching, two-dimensional (2-D) digital filtering, morphologic operations, and motion estimation share some common properties. They can all benefit from using reconfigurable computers that use coprocessor boards based on field-programmable gate array (FPGA) chips. This paper characterizes those applications as generalized template matching (GTM) operations and describes the mapping of the GTM operations onto reconfigurable computers. A three-step approach is described. The first two steps enumerate and prune the design space of basic GTM building blocks, which consist of FPGA buffers and GTM computation cores. The last step is to achieve a solution through an optimal combination of these building blocks where the cost function is the FPGA computation time and the constraints are FPGA coprocessor board resources. Various FPGA buffers are presented so as to introduce design options of basic GTM building blocks. Algorithms used for the mapping are described. Experimental results are summarized to reveal the relationship between the GTM mapping results and FPGA board resource parameters.

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What this paper is about

Image processing algorithms for template matching, two-dimensional (2-D) digital filtering, morphologic operations, and motion estimation share some common properties. They can all benefit from using reconfigurable computers that use coprocessor boards based on field-programmable gate array (FPGA) chips. This paper characterizes those applications as generalized template matching (GTM) operations and describes the mapping of the GTM operations onto reconfigurable computers. A three-step approach is described. The first two steps enumerate and prune the design space of basic GTM building blocks, which consist of FPGA buffers and GTM computation cores. The last step is to achieve a solution through an optimal combination of these building blocks where the cost function is the FPGA computation time and the constraints are FPGA coprocessor board resources. Various FPGA buffers are presented so as to introduce design options of basic GTM building blocks. Algorithms used for the mapping are described. Experimental results are summarized to reveal the relationship between the GTM mapping results and FPGA board resource parameters.

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Available abstract

Image processing algorithms for template matching, two-dimensional (2-D) digital filtering, morphologic operations, and motion estimation share some common properties. They can all benefit from using reconfigurable computers that use coprocessor boards based on field-programmable gate array (FPGA) chips. This paper characterizes those applications as generalized template matching (GTM) operations and describes the mapping of the GTM operations onto reconfigurable computers. A three-step approach is described. The first two steps enumerate and prune the design space of basic GTM building blocks, which consist of FPGA buffers and GTM computation cores. The last step is to achieve a solution through an optimal combination of these building blocks where the cost function is the FPGA computation time and the constraints are FPGA coprocessor board resources. Various FPGA buffers are presented so as to introduce design options of basic GTM building blocks. Algorithms used for the mapping are described. Experimental results are summarized to reveal the relationship between the GTM mapping results and FPGA board resource parameters.

Key concepts: Field-programmable gate array, Coprocessor, Computer science, Computation, Reconfigurable computing, Gate array, Matching (statistics), Computer hardware

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