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

Wang, Qiuping; Wang, Tiepeng; Zhang, Ke

doi:10.1108/03684921211243301

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…Its purpose is to predict data that is irregular but changes monotonically. The GM (1,1) model is the fundamental and most widely used grey prediction model; many prediction models have been derived based on the original GM (1,1) such as the mean GM (1,1) model (EGM), the original difference GM (1,1) model (ODGM), mean difference model (EDGM), discrete GM (1,1) model (DGM), and grey Verhulst model [37].…”

Section: A Improved Grey Prediction Modelmentioning

confidence: 99%

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Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

et al. 2020

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Existing edge detection algorithms suffer from inefficient edge localization, noise sensitivity, and/or relatively poor automatic detection capability. Contemporary edge detection algorithms can be improved by targeting these problems to help bolster their performance. Grey system theory can be used to resolve the small data and poor information issues in the local information of uncertain systems. An automatic edge detection algorithm was developed in this study based on a grey prediction model to remedy these problems. Noise characteristics in grey images are used to deploy a noise-filtering algorithm based on local features. A mask with twenty-four edge direction information points (345°) was established based on edge line texture features. By compressing the amplitude of the sequence, the randomly oscillated grey prediction sequence can be converted into a smooth, new sequence. The discrete grey model (1,1) (DGM(1,1)) was established based on this new grey prediction sequence to obtain the grey prediction maximum value. A grey prediction image with enhanced edges was obtained by replacing the pixel value in the original image with the maximum grey prediction value. A grey prediction subtraction image with edges separated from non-edge points was also obtained by subtracting the original image from the grey prediction image. The optimal separation threshold in the grey prediction subtraction image can be determined via the global adaptive threshold selection method. The neighborhood search method was then deployed to remove stray points and burrs from the image after the target was separated from the background, creating the final edge image. Experiments were performed on a computer-simulated phantom to find that both the subjective visual effects and objective evaluation criteria are better under the proposed method than several other competitive methods. The proposed edge detection algorithm shows excellent edge detection ability and is highly robust to noise, though the grey prediction model needs further improvement to optimize the run time.

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Section: A Improved Grey Prediction Modelmentioning

confidence: 99%

“…The original form of the GM (1,1) model is essentially a difference equation [37], [38]. Because the gray values in the 3X3 neighborhood window are discrete data and nonhomogeneous exponential sequences, the algorithm proposed in this paper uses the DGM (1,1) model [39].…”

Section: A Improved Grey Prediction Modelmentioning

confidence: 99%

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

et al. 2020

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“…is the original non-negative data series taken in consecutive order and at equal time interval [10].…”

Section: Construction and Testing Of Grey Prediction Model Gm(11)mentioning

confidence: 99%

Demand Forecasting of Agricultural Products Logistics in Community

Xu¹,

Piao²,

Song³

2015

AJIBM

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This paper discusses the characteristics of the community agricultural products logistics and the difficulty of the demand forecasting of agricultural products logistics in community. Obtaining the data over the years relates to agricultural products logistics in community through questionnaires. Then using Grey prediction model conduct the demand forecasting of agricultural products logistics in community. The study of this paper can provide reference data for the agricultural outlets near the community in Beijing, solve the problem of matching supply and demand of community shops, satisfy consumer's demand and maximize the benefits of operators. The study of this paper can provide reference data for the agricultural outlets near the community in Beijing, solving the matching supply and demand problems of community shops, satisfying consumer's demand and maximizing the benefits of operators. Besides, it can also provide some references meaning for e-commerce companies of Beijing, such as the location of community convenient store in O2O e-commerce model, the solution of "The last mile" and so on.

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“…The contour-enhanced image was obtained, and the image edge information was clearly detected [24]. Wang et al effectively merged the discrete wavelet transform (DWT) with the grey prediction model to suggest a more reliable image edge detection approach [25].…”

Section: Introductionmentioning

confidence: 99%

An Image Edge Detection Method Based on Fractional-Order Grey System Model

Jia

Pang

2022

Electronics

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The detection of edges in images is a pressing issue in the field of image processing. This technique has found widespread application in image pattern recognition, machine vision, and a variety of other areas. The feasibility and effectiveness of grey theory in image engineering applications have prompted researchers to continuously explore it. The grey model (GM (1,1)) with the first-order differentiation of one variable is the grey prediction model that is most frequently used. It is a typical trend analysis model and can be used for image edge detection. The traditional integer-order differential image edge detection operator has problems such as blurred and discontinuous edges, incomplete image details, and high influence by noise. We present a novel grey model for detecting image edges based on a fractional-order discrete operator in this paper. To improve the features of the original image, our model first preprocesses it before calculating the prediction of the original image using our fractional-order cumulative greyscale model. We obtain the edge information of the image by first subtracting a preprocessed image from the predicted image and then eliminating isolated noise points using the median filtering method. Based on the discrete wavelet transform, image edges are finally extracted. The comparison experiments with a traditional edge detection operator show that our algorithm can accurately locate the image edges, the image edges are clear and complete, and this model has better anti-noise performance.

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

Cited by 13 publications

References 7 publications

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

Demand Forecasting of Agricultural Products Logistics in Community

An Image Edge Detection Method Based on Fractional-Order Grey System Model

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