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Proceedings Paper

Real-time machine vision system using FPGA and soft-core processor
Author(s): Abdul Waheed Malik; Benny Thörnberg; Xiaozhou Meng; Muhammad Imran
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Paper Abstract

This paper presents a machine vision system for real-time computation of distance and angle of a camera from reference points in the environment. Image pre-processing, component labeling and feature extraction modules were modeled at Register Transfer (RT) level and synthesized for implementation on field programmable gate arrays (FPGA). The extracted image component features were sent from the hardware modules to a soft-core processor, MicroBlaze, for computation of distance and angle. A CMOS imaging sensor operating at a clock frequency of 27MHz was used in our experiments to produce a video stream at the rate of 75 frames per second. Image component labeling and feature extraction modules were running in parallel having a total latency of 13ms. The MicroBlaze was interfaced with the component labeling and feature extraction modules through Fast Simplex Link (FSL). The latency for computing distance and angle of camera from the reference points was measured to be 2ms on the MicroBlaze, running at 100 MHz clock frequency. In this paper, we present the performance analysis, device utilization and power consumption for the designed system. The FPGA based machine vision system that we propose has high frame speed, low latency and a power consumption that is much lower compared to commercially available smart camera solutions.

Paper Details

Date Published: 1 May 2012
PDF: 8 pages
Proc. SPIE 8437, Real-Time Image and Video Processing 2012, 84370Z (1 May 2012); doi: 10.1117/12.927854
Show Author Affiliations
Abdul Waheed Malik, Mid Sweden Univ. (Sweden)
Benny Thörnberg, Mid Sweden Univ. (Sweden)
Xiaozhou Meng, Mid Sweden Univ. (Sweden)
Muhammad Imran, Mid Sweden Univ. (Sweden)

Published in SPIE Proceedings Vol. 8437:
Real-Time Image and Video Processing 2012
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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