An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA Implementation

For improving the processing speed and accuracy of edge detection, an adaptive edge detection method based on improved NMS (nonmaximum suppression) was proposed in this paper. In the method, the gradient image was computed by four directional Sobel operators. Then, the gradient image was processed b...

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Main Authors: Enzeng Dong, Yao Zhao, Xiao Yu, Junchao Zhu, Chao Chen
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Journal of Sensors
Online Access:http://dx.doi.org/10.1155/2016/1470312
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spelling doaj-188f0e60c3ca4db4a55e58403d72118c2020-11-24T23:27:10ZengHindawi LimitedJournal of Sensors1687-725X1687-72682016-01-01201610.1155/2016/14703121470312An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA ImplementationEnzeng Dong0Yao Zhao1Xiao Yu2Junchao Zhu3Chao Chen4Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin 300384, ChinaKey Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin 300384, ChinaKey Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin 300384, ChinaKey Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin 300384, ChinaKey Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin 300384, ChinaFor improving the processing speed and accuracy of edge detection, an adaptive edge detection method based on improved NMS (nonmaximum suppression) was proposed in this paper. In the method, the gradient image was computed by four directional Sobel operators. Then, the gradient image was processed by using NMS method. By defining a power map function, the elements values of gradient image histogram were mapped into a wider value range. By calculating the maximal between-class variance according to the mapped histogram, the corresponding threshold was obtained as adaptive threshold value in edge detection. Finally, to be convenient for engineering application, the proposed method was realized in FPGA (Field Programmable Gate Array). The experiment results demonstrated that the proposed method was effective in edge detection and suitable for real-time application.http://dx.doi.org/10.1155/2016/1470312
collection DOAJ
language English
format Article
sources DOAJ
author Enzeng Dong
Yao Zhao
Xiao Yu
Junchao Zhu
Chao Chen
spellingShingle Enzeng Dong
Yao Zhao
Xiao Yu
Junchao Zhu
Chao Chen
An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA Implementation
Journal of Sensors
author_facet Enzeng Dong
Yao Zhao
Xiao Yu
Junchao Zhu
Chao Chen
author_sort Enzeng Dong
title An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA Implementation
title_short An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA Implementation
title_full An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA Implementation
title_fullStr An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA Implementation
title_full_unstemmed An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA Implementation
title_sort improved nms-based adaptive edge detection method and its fpga implementation
publisher Hindawi Limited
series Journal of Sensors
issn 1687-725X
1687-7268
publishDate 2016-01-01
description For improving the processing speed and accuracy of edge detection, an adaptive edge detection method based on improved NMS (nonmaximum suppression) was proposed in this paper. In the method, the gradient image was computed by four directional Sobel operators. Then, the gradient image was processed by using NMS method. By defining a power map function, the elements values of gradient image histogram were mapped into a wider value range. By calculating the maximal between-class variance according to the mapped histogram, the corresponding threshold was obtained as adaptive threshold value in edge detection. Finally, to be convenient for engineering application, the proposed method was realized in FPGA (Field Programmable Gate Array). The experiment results demonstrated that the proposed method was effective in edge detection and suitable for real-time application.
url http://dx.doi.org/10.1155/2016/1470312
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