Edge Detection Algorithm based on Morphology and Grey Relation Analysis

Zheng, Zhen; Zha, Bingting; Yuan, Hailu; Xuchen, Youshi

doi:10.1109/icma.2019.8816311

Cited by 5 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to verify the performance of the proposed algorithm, we compared it to other state-of-the-art algorithms including the improved Canny algorithm proposed by Rong et al [12], the traditional GM (1,1) edge detection algorithm (threshold th=5) proposed by Wan et al [38], the traditional GRA edge detection algorithm (threshold k=0.1) proposed by Li et al [32], the morphological method proposed by Zheng et al [19], and the Zernike moment method proposed by Peng et al [16]. Fig.…”

Section: A Actual Image Edge Detectionmentioning

confidence: 99%

“…An appropriate multi-scale morphological operator must be selected based on mathematical morphology, at which point the edge positioning ability is strong and the amount of calculation is small, but the edges appear to be rough. To improve the stability of the edge-detector, an appropriate filter can be applied as a pre-operation [19]- [21]. Zheng et al [19] designed an edge refinement method for this purpose which is effective to some extent but not robust to noise.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the stability of the edge-detector, an appropriate filter can be applied as a pre-operation [19]- [21]. Zheng et al [19] designed an edge refinement method for this purpose which is effective to some extent but not robust to noise. Edge detection algorithms based on wavelet transform or neural network methods show better edge localization ability and noisesensitivity performance, but their calculation burden is so large that the processing time is too long for practical application.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: A Actual Image Edge Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ruipu Tan et al proposed evaluating disasters caused by typhoons using a decision method based on entropic information and GRA [17]. Zhen Zheng et al used GRA to denoise the images, then, they adaptively converted them into binary images, and detected the edge parts of the images using the morphological calculation model [18]. The data concerning the dependent variables in GRA needs to be one-dimensional, whereas the data describing typhoon movement when analyzing the correlation between typhoon tracks and physical variables is two-dimensional; therefore, the correlation cannot be directly analyzed by GRA.…”

Section: Introductionmentioning

confidence: 99%

Typhoon Tracks Prediction with ConvLSTM Fused Reanalysis Data

Sun

et al. 2022

Electronics

View full text Add to dashboard Cite

Typhoon occurrences pose a great threat to people’s lives and property; therefore, it is important to predict typhoon tracks accurately for disaster prevention and reduction. In recent years, research using traditional machine learning methods has struggled to include temporal and spatial features. Moreover, research that has been conducted using satellite images only does not consider the influence of physical factors on typhoon movement; therefore, this paper proposes to add a convolutional layer to the Convolutional LSTM (ConvLSTM) model to improve the ability of the model to extract images. The previous positions of the typhoon’s center are marked on subsequent reanalysis images. The subsequent coordinates of the typhoon’s center are found by fitting the predicted coordinates of each physical variable. The research method in this paper required selecting the physical variables group which was most correlated with the direction and distance of the typhoon movement from 11 physical variables; this was achieved using Canonical Correlation Analysis (CCA) and Grey Relation Analysis (GRA). Then, reanalysis data is transformed into images and a continuous series of reanalysis image sequences is inputted into the ConvLSTM model so that it can make predictions. The mean absolute error of distance used for the ERA5 dataset, using the method proposed, was 54.69 km; thus, the validity of the model was proven.

show abstract