Adaptive threshold binarization and morphological image processing based on FPGA

Home > Archive>Volume 39, Issue 7, 2016 >67-71

Adaptive threshold binarization and morphological image processing based on FPGA
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                        Chen Xinyuan1Chen Xinyuan
School of OpticalElectrical and computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Li Jun1Li Jun
School of OpticalElectrical and computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Yang HaimaYang Haima
1. School of OpticalElectrical and computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. Shanghai Institute of Technical Physics of Chinese Academy of Sciences, Shanghai 200083, China
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Song Jia1Song Jia
School of OpticalElectrical and computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Shao Luxian1Shao Luxian
School of OpticalElectrical and computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Affiliation:1. School of OpticalElectrical and computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. Shanghai Institute of Technical Physics of Chinese Academy of Sciences, Shanghai 200083, China
Clc Number:TP391.4
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Abstract:

For reducing a fixed threshold binarization method’s drawback and the serious Inadequate and Interference after binarization handling,meanwhile the traditional DSP and other processors in highspeed image processing in realtime poor is not good enough, proposing that using FPGA / Nios II processor and the improved adaptive threshold Sobel algorithm for image binarization, morphological processing again. The whole system implementation includes the original image capture, color format conversion, gradation processing, edge detection and median filtering using the weighted average method and adaptive threshold image binarization, and finally by using morphological methods to further deal with the binary image, to achieve a more efficient extraction of contour, and through VGA interface display. Experimental results show that the system is high efficient, fast, stable and reliable.

Key words:binarization; FPGA/Nios II; adaptive threshold;morphology; edge detection; median filtering and weighted average

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Online: August 17,2016
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