A methodology for speeding up edge and line detection algorithms focusing on memory architecture utilization.

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Bibliographic Details
Title:	A methodology for speeding up edge and line detection algorithms focusing on memory architecture utilization.
Authors:	Kelefouras, Vasilios¹ kelefouras@ece.upatras.gr, Kritikakou, Angeliki¹, Goutis, Costas¹
Source:	Journal of Supercomputing. Apr2014, Vol. 68 Issue 1, p459-487. 29p.
Subjects:	Library software, Memory hierarchy (Computer science), Field programmable gate arrays, Computer vision, Computer storage devices
Abstract:	In this paper, a new methodology for speeding up edge and line detection algorithms is presented, achieving improved performance over the state of the art software library OpenCV (speedup from 1.35 up to 2.22) and other conventional implementations, in both general and embedded processors, by reducing the number of load/store and arithmetic instructions, the number of data cache accesses and data cache misses in memory hierarchy and the algorithm memory size. This is achieved by fully exploiting the combination of the software and hardware parameters which are considered simultaneously as one problem and not separately. Furthermore, the edge and line detection algorithms have been simplified for a computer vision application in a Virtex-5 Xilinx FPGA using Microblaze soft processor (detection and measurement of flow fronts in a microfluid device); it achieves speedup up to 660 times in comparison with conventional software implementations. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	In this paper, a new methodology for speeding up edge and line detection algorithms is presented, achieving improved performance over the state of the art software library OpenCV (speedup from 1.35 up to 2.22) and other conventional implementations, in both general and embedded processors, by reducing the number of load/store and arithmetic instructions, the number of data cache accesses and data cache misses in memory hierarchy and the algorithm memory size. This is achieved by fully exploiting the combination of the software and hardware parameters which are considered simultaneously as one problem and not separately. Furthermore, the edge and line detection algorithms have been simplified for a computer vision application in a Virtex-5 Xilinx FPGA using Microblaze soft processor (detection and measurement of flow fronts in a microfluid device); it achieves speedup up to 660 times in comparison with conventional software implementations. [ABSTRACT FROM AUTHOR]
ISSN:	09208542
DOI:	10.1007/s11227-013-1049-x