Image edge extraction based on data field theory
and Euclidean distance

Computer Engineering & Science

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Image edge extraction based on data field theory

and Euclidean distance

HUANG Shan1,LI Zhong1，HUANG Mengmeng2

（1.School of Electronics and Information,Jiangsu University of Science and

Technology,Zhenjiang 212003,China;

2.Information support station，Troop 94906,Suzhou，215000，China）

Received:2015-09-08 Revised:2015-11-05 Online:2016-11-25 Published:2016-11-25

Abstract

Abstract:

Image edges are the basis of image analysis and recognition,therefore the accuracy and

continuity of image edge information can affect subsequent image processing operations such

as image analysis and recognition. In order to realize the effective extraction of image

edges,we propose an image edge extraction method based on the data field theory and the

Euclidean distance.We first construct the image data field based on the data field theory

and convert the gray space to the data field potential space.We then employ the Euclidean

distance to calculate the potential value of the image data field.The introduction of image

Euclidean distance in the calculation of data quality can expand pixel difference and

suppress small and trivial details and noise,thus obtaining a potential value graph with

separated “background” and “target” potential values.Finally we utilize the improved

Canny operator to extract the edge.Experimental results show that the proposed method can

effectively improve the accuracy of edge extraction,reduce false edges,and suppress

redundant details and noise.

Key words:

data field, potential value graph, Euclidean distance of images, improved Canny operator, edge

extraction

HUANG Shan1,LI Zhong1，HUANG Mengmeng2.

Image edge extraction based on data field theory

and Euclidean distance

[J]. Computer Engineering & Science.

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Image edge extraction based on data field theory

and Euclidean distance

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