research-article

An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics

Authors:

Hong LiuAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 29

Pages 5206 - 5215

https://doi.org/10.1109/TIP.2020.2980170

Published: 01 January 2020 Publication History

Abstract

Edge detection is one of the most fundamental operations in the field of image analysis and computer vision as a critical preprocessing step for high-level tasks. It is difficult to give a generic threshold that works well on all images as the image contents are totally different. This paper presents an adaptive, robust and effective edge detector for real-time applications. According to the 2D entropy, the images can be clarified into three groups, each attached with a reference percentage value based on the edge proportion statistics. Compared with the attached points along the gradient direction, anchor points were extracted with high probability to be edge pixels. Taking the segment direction into account, these points were then jointed into different edge segments, each of which was a clean, contiguous, 1-pixel wide chain of pixels. Experimental results indicate that the proposed edge detector outperforms the traditional edge following methods in terms of detection accuracy. Besides, the detection results can be used as the input information for post-processing applications in real-time.

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An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics
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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 29, Issue

2020

3918 pages

ISSN:1057-7149

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1057-7149 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 January 2020

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Cited By

Xian WZhou MFang BXiang TJia WChen B(2024)Perceptual Quality Analysis in Deep Domains Using Structure Separation and High-Order MomentsIEEE Transactions on Multimedia10.1109/TMM.2023.329373026(2219-2234)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3293730
M KMS S(2024)Bernstein approximation-based adaptive local thresholding for enhanced edge detectionComputers and Electrical Engineering10.1016/j.compeleceng.2024.109397118:PAOnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109397
Yang WChen XWang HMao X(2024)Edge detection using multi-scale closest neighbor operator and grid partitionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02894-y40:3(1947-1964)Online publication date: 1-Mar-2024
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Teng LQiao YShafiq MSrivastava GJaved AGadekallu TYin S(2023)FLPK-BiSeNet: Federated Learning Based on Priori Knowledge and Bilateral Segmentation Network for Image Edge ExtractionIEEE Transactions on Network and Service Management10.1109/TNSM.2023.327399120:2(1529-1542)Online publication date: 1-Jun-2023
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