Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t...Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.展开更多
Combining beamlet transform with steerable filters, a new edge detection method based on line gradient is proposed. Compared with operators based on point local properties, the edge-detection results with this method ...Combining beamlet transform with steerable filters, a new edge detection method based on line gradient is proposed. Compared with operators based on point local properties, the edge-detection results with this method achieve higher SNR and position accuracy, and are quite helpful for image registration, object identification, etc. Some edge-detection experiments on optical and SAR images that demonstrate the significant improvement over classical edge operators axe also presented. Moreover, the template matching result based on edge information of optical reference image and SAR image also proves the validity of this method.展开更多
This paper proposes a mem-computing model of memristive network-based genetic algorithm(MNGA)by building up the relationship between the memristive network(MN)and the genetic algorithm(GA),and a new edge detection alg...This paper proposes a mem-computing model of memristive network-based genetic algorithm(MNGA)by building up the relationship between the memristive network(MN)and the genetic algorithm(GA),and a new edge detection algorithm where image pixels are defined as individuals of population.First,the computing model of MNGA is designed to perform mem-computing,which brings new possibility of the hardware implementation of GA.Secondly,MNGA-based edge detection integrating image filter and GA operator deployed by MN is proposed.Finally,simulation results demonstrate that the figure of merit(FoM)of our model is better than the latest memristor-based swarm intelligence.In summary,a new way is found to build proper matching of memristor to GA and aid image edge detection.展开更多
A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected...A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.展开更多
Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application t...Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.展开更多
To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform ...To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.展开更多
A new edge detection method combining the scanning window central edge （SWCE） detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pi...A new edge detection method combining the scanning window central edge （SWCE） detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pixel value means to perform an optimal edge detection, and then proposes an improved active contour model with modified energy functions to refine the location of the edges. The initial nodes of the improved active contour model are automatically found from the vectorised results of the SWCE detector. Tests on simulated speckled images and real airborne SAR images show that the combined method can benefit from the advantages of the both techniques and get satisfactory edge detection and localization abilities at the same time.展开更多
Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due ...Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.展开更多
The zinc casting is a complicated process with high temperature, high dust content and dynamic solidification. To accurately detect the edge and texture of metal image under this condition, a sub-pixel detection based...The zinc casting is a complicated process with high temperature, high dust content and dynamic solidification. To accurately detect the edge and texture of metal image under this condition, a sub-pixel detection based on gradient entropy and adaptive four-order cubic convolution interpolation （GEAF-CCI） algorithm is proposed. This method mainly involves three procedures. Firstly, the gradient image is generated from the grey images by using gradient operator. Then, a dynamic threshold based on the maximum local gradient entropy （DTMLGE） algorithm is applied to distinguishing the edge and texture pixels from gradient images. Finally, the adaptive four-order cubic convolution interpolation （AF-CCI） algorithm is proposed for interpolating calculation of the target edges and textures according to their variation differences in different directions. The experimental result shows that the proposed algorithm can remove the jag and blur of the edges and textures, improve the edge positioning precision and reduce the false or missing detection rate.展开更多
With the increasing necessities for reliable printed circuit board(PCB) product, there has been a considerable demand for high speed and high precision vision positioning system. To locate a rectangular lead component...With the increasing necessities for reliable printed circuit board(PCB) product, there has been a considerable demand for high speed and high precision vision positioning system. To locate a rectangular lead component with high accuracy and reliability, a new visual positioning method was introduced. Considering the limitations of Ghosal sub-pixel edge detection algorithm, an improved algorithm was proposed, in which Harris corner features were used to coarsely detect the edge points and Zernike moments were adopted to accurately detect the edge points. Besides, two formulas were developed to determine the edge intersections whose sub-pixel coordinates were calculated with bilinear interpolation and conjugate gradient method. The last experimental results show that the proposed method can detect the deflection and offset, and the detection errors are less than 0.04° and 0.02 pixels.展开更多
基金supported by the National Natural Science Foundation of China(62276055).
文摘Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
文摘Combining beamlet transform with steerable filters, a new edge detection method based on line gradient is proposed. Compared with operators based on point local properties, the edge-detection results with this method achieve higher SNR and position accuracy, and are quite helpful for image registration, object identification, etc. Some edge-detection experiments on optical and SAR images that demonstrate the significant improvement over classical edge operators axe also presented. Moreover, the template matching result based on edge information of optical reference image and SAR image also proves the validity of this method.
基金This work was supported by the National Natural Science Foundation of China(61550110248)the Sichuan Science and Technology Department project(2019YFG0190)the University of Electronic Science and Technology of China project(H04W170186).
文摘This paper proposes a mem-computing model of memristive network-based genetic algorithm(MNGA)by building up the relationship between the memristive network(MN)and the genetic algorithm(GA),and a new edge detection algorithm where image pixels are defined as individuals of population.First,the computing model of MNGA is designed to perform mem-computing,which brings new possibility of the hardware implementation of GA.Secondly,MNGA-based edge detection integrating image filter and GA operator deployed by MN is proposed.Finally,simulation results demonstrate that the figure of merit(FoM)of our model is better than the latest memristor-based swarm intelligence.In summary,a new way is found to build proper matching of memristor to GA and aid image edge detection.
基金supported partly by the National Basic Research Program of China (2005CB724303)the National Natural Science Foundation of China (60671062) Shanghai Leading Academic Discipline Project (B112).
文摘A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.
基金supported by the Research Fund for International Young Scientists of the National Natural Science Foundation of China(61550110248)the Research on Fundamental Theory of Shared Intelligent Street Lamp for New Scene Service(H04W200495)+1 种基金Sichuan Science and Technology Program(2019YFG0190)the Research on Sino-Tibetan Multi-source Information Acquisition,Fusion,Data Mining and its Application(H04W170186).
文摘Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.
基金supported by the National Natural Science Foundation of China(6067309760702062)+3 种基金the National HighTechnology Research and Development Program of China(863 Program)(2008AA01Z1252007AA12Z136)the National ResearchFoundation for the Doctoral Program of Higher Education of China(20060701007)the Program for Cheung Kong Scholarsand Innovative Research Team in University(IRT 0645).
文摘To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.
文摘A new edge detection method combining the scanning window central edge （SWCE） detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pixel value means to perform an optimal edge detection, and then proposes an improved active contour model with modified energy functions to refine the location of the edges. The initial nodes of the improved active contour model are automatically found from the vectorised results of the SWCE detector. Tests on simulated speckled images and real airborne SAR images show that the combined method can benefit from the advantages of the both techniques and get satisfactory edge detection and localization abilities at the same time.
基金supported by the National Natural Science Foundation of China(61210012)
文摘Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.
基金Project（61673400） supported by the National Natural Science Foundation of China Project（61590923） supported by the Major Program of the National Natural Science Foundation of China+2 种基金 Project（61621062） supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China Project（61533020） supported by the State Key Program of National Natural Science of China Project（502221709） supported by the Fundamental Research Funds for the Central Universities, China
文摘The zinc casting is a complicated process with high temperature, high dust content and dynamic solidification. To accurately detect the edge and texture of metal image under this condition, a sub-pixel detection based on gradient entropy and adaptive four-order cubic convolution interpolation （GEAF-CCI） algorithm is proposed. This method mainly involves three procedures. Firstly, the gradient image is generated from the grey images by using gradient operator. Then, a dynamic threshold based on the maximum local gradient entropy （DTMLGE） algorithm is applied to distinguishing the edge and texture pixels from gradient images. Finally, the adaptive four-order cubic convolution interpolation （AF-CCI） algorithm is proposed for interpolating calculation of the target edges and textures according to their variation differences in different directions. The experimental result shows that the proposed algorithm can remove the jag and blur of the edges and textures, improve the edge positioning precision and reduce the false or missing detection rate.
基金Project(51175242)supported by the National Natural Science Foundation of ChinaProject(BA2012031)supported by the Jiangsu Province Science and Technology Foundation of China
文摘With the increasing necessities for reliable printed circuit board(PCB) product, there has been a considerable demand for high speed and high precision vision positioning system. To locate a rectangular lead component with high accuracy and reliability, a new visual positioning method was introduced. Considering the limitations of Ghosal sub-pixel edge detection algorithm, an improved algorithm was proposed, in which Harris corner features were used to coarsely detect the edge points and Zernike moments were adopted to accurately detect the edge points. Besides, two formulas were developed to determine the edge intersections whose sub-pixel coordinates were calculated with bilinear interpolation and conjugate gradient method. The last experimental results show that the proposed method can detect the deflection and offset, and the detection errors are less than 0.04° and 0.02 pixels.
