Edge Detection Algorithm Based on Grey System Theory Combined with Directed Graph

Yan, Wei; Jia, Shanshan; Wang, Dong

doi:10.1109/icig.2013.42

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Considering the directions meeting the equation (8), and finding out the directions which are impossible to become edge. Suppose…”

Section: The Extraction Of Edge From the Feature Imagementioning

confidence: 99%

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A New Edge Detection Method Based on Hausdorff Distance

You¹,

Qing-xin²,

Guo³

2014

2014 5th International Conference on Digital Home

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Most edge detection methods are based on firstorder or second-order differential. These are local methods. Using Hausdorff distance to quantify the strength of the edge is a method with a holistic property. Firstly, downsample the image, and split the image into two sets. Secondly, get the feature image by assigning a value for each point using the scalar field map constructed by Hausdorff distance. Since the edge features have local properties, in this paper, we constructed a map which can get local feature images using sub image and combined them into a feature image. Finally we present a method to get the edge image by feature image. We experimentally verify the feasibility of this method.

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“…Considering the directions meeting the equation (8), and finding out the directions which are impossible to become edge. Suppose…”

Section: The Extraction Of Edge From the Feature Imagementioning

confidence: 99%

“…The traditional Canny operator has the defect that being vulnerable to various noise disturbances [4], [5]. Many new methods of edge detection emerged recent years, for example, edge detection method based on morphology [6], edge detection using gray system theory [7], [8], etc [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

A New Edge Detection Method Based on Hausdorff Distance

You¹,

Qing-xin²,

Guo³

2014

2014 5th International Conference on Digital Home

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Autonomous System Enabling Node and Edge Detection, Path Optimization, and Effective Color-Coded Box Management in Diverse Robotic Environments

Jayanth,

Mundra,

Sowmya

et al. 2024

Lecture Notes in Networks and Systems

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Adaptive Edge Detection Algorithm Based on Grey Entropy Theory and Textural Features

et al. 2019

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The traditional edge detection method is altogether inaccurate, nonadaptive, and particularly ineffective on noisy images. This paper proposes a novel edge detection algorithm based on gray entropy theory and local texture features. In the 3×3 neighborhood window, 28 comparison sequences are constructed according to local texture features. The reference sequence is composed of the median of all elements in the 3×3 neighborhood window. A total of 28 gray relation degrees as obtained by gray relation analysis between the 28 comparison sequences and reference sequences, as well as 28 gray relation degrees, are analyzed by gray entropy theory to initially filter the image. Gray entropy analysis is then performed on the comparison sequences composed of 28 texture features and reference sequences composed of the central pixel points of the filtered image to determine the maximum gray entropy difference. A comparative threshold adaptive acquisition method is designed to separate gray entropy difference sequence elements and identify all edge points accordingly. The simulation results show that the proposed algorithm effectively achieves adaptive edge detection and has strong anti-noise capability. The results of this study may provide a workable reference for edge information detection in the field of artificial intelligence (e.g., image recognition, pattern recognition applications).INDEX TERMS Image processing, image edge detection, gray relation analysis, gray entropy theory, textural feature analysis.

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Edge Detection Algorithm Based on Grey System Theory Combined with Directed Graph

Cited by 7 publications

References 13 publications

A New Edge Detection Method Based on Hausdorff Distance

A New Edge Detection Method Based on Hausdorff Distance

Autonomous System Enabling Node and Edge Detection, Path Optimization, and Effective Color-Coded Box Management in Diverse Robotic Environments

Adaptive Edge Detection Algorithm Based on Grey Entropy Theory and Textural Features

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