An Improved Method for Image Edge Detection Based on GM(1,1) Model

Zhang, Jianxin; Wu, Lang

doi:10.1109/aici.2009.220

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…The GM(1,1) model is one of the important contents in grey system theory and it has been widely used in many fields including fault prediction [1].…”

Section: Basic Principle Of Grey Model Predictionmentioning

confidence: 99%

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Research on Fault Prediction of Modern Aviation Electronic Equipment Based on Improved Grey Model

Zhou¹,

Jing²,

Xin-hua³

et al. 2013

JSEA

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The basic principle and method of Grey Model prediction are presented. In view of the defects of general GM(1,1) model, an improved method is proposed. That is using the particle swarm optimization algorithm to obtain the best forecast dimension and using metabolism to make the model parameters adaptively change. Finally, the improved Grey Model is used to predict the fault of high voltage power supply circuit of a certain type of modern air-borne radar. The results which are computed and simulated by Matlab software show that the forecast precision of improved Grey Model is higher than that of original Grey Model

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“…The GM(1,1) model is one of the important contents in grey system theory and it has been widely used in many fields including fault prediction [1].…”

Section: Basic Principle Of Grey Model Predictionmentioning

confidence: 99%

“…Firstly, use the initial sequence {x (0) (l), x (0) (2), … , x (0) (m)} to establish GM(1,1) and to predict (0) ( 1) x m . Secondly, use sequence {x (0)…”

Section: Metabolismmentioning

confidence: 99%

Research on Fault Prediction of Modern Aviation Electronic Equipment Based on Improved Grey Model

Zhou¹,

Jing²,

Xin-hua³

et al. 2013

JSEA

View full text Add to dashboard Cite

show abstract

“…The Grey prediction model is applied in a variety of fields for its ability of generating predicted value of a sequence under limited information or sampled values [ 20 , 21 ]. With this characteristic, some of the researches apply the Grey prediction model for the edge detection of an image so that the discontinuity or change in intensity can be highlighted [ 22 – 24 ]. Instead of using the Grey prediction model for finding discontinuities in an image directly, in this paper we apply the use of a GM(1,1) model for the determination of the maximal additive value Δ.…”

Section: Introductionmentioning

confidence: 99%

A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images

Kau

Lee

2014

The Scientific World Journal

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Aimed to find the additive magnitude automatically and adaptively, we propose a three-step and model-based approach for the sharpening of images in this paper. In the first pass, a Grey prediction model is applied to find a global maximal additive magnitude so that the condition of oversharpening in images to be sharpened can be avoided. During the second pass, edge pixels are picked out with our previously proposed edge detection mechanism. In this pass, a low-pass filter is also applied so that isolated pixels will not be regarded as around an edge. In the final pass, those pixels detected as around an edge are adjusted adaptively based on the local statistics, and those nonedge pixels are kept unaltered. Extensive experiments on natural images as well as medical images with subjective and objective evaluations will be given to demonstrate the usefulness of the proposed approach.

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“…The image of contour enhancement was obtained by adjusting parameter p and selecting image data in different directions, and then image edge information was detected in effect. Zhang and Wu (2009) used boundary value x (1) (n) instead of the initial value x (1) (1) in GM (1,1). Detected the image edge by using the improved GM (1,1) model along eight directions, respectively.…”

Section: Introductionmentioning

confidence: 99%

Image edge detection based on the grey prediction model and discrete wavelet transform

Wang

Wang²,

Zhang³

2012

Kybernetes

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Purpose -Image edge detection is an essential issue in image processing and computer vision. The purpose of this paper is to provide a novel and effective algorithm for image edge detection. Design/methodology/approach -Because GM (1,1) model is a typical model for tendency analysis, GM (1,1) model can be used for detecting edge. Prediction image data are close to the original image data by reason of the data being smooth in the non-edge zone of image. The principle of edge detection by GM (1,1) model is that the predicted value at an edge point will be an overestimate or underestimate owing to the data changing drastically in the edge zone of the image. First, the edge image information is obtained by a preprocessed image subtracting from prediction image via GM (1,1). Second, median filter is used to eliminate isolated point noise in edge information images, and discrete wavelet transform is used to extract the image edge. Finally, this paper verifies the proposed algorithm by experiment. Findings -Experimental results show that the proposed algorithm has advantages such as precisely locating, abundant weak edge, and better anti-noise performance. Practical implications -The algorithm proposed in the paper can precisely detect the information of edge image, and get a clear image detail. Originality/value -Grey system theory developed vigorously lays the foundation for image processing. Wavelet analysis in image processing has its characteristics. This paper combines grey prediction model with discrete wavelet transform (DWT) successfully and obtains a novel and effective algorithm for image edge detection.

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An Improved Method for Image Edge Detection Based on GM(1,1) Model

Cited by 10 publications

References 6 publications

Research on Fault Prediction of Modern Aviation Electronic Equipment Based on Improved Grey Model

Research on Fault Prediction of Modern Aviation Electronic Equipment Based on Improved Grey Model

A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images

Image edge detection based on the grey prediction model and discrete wavelet transform

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