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An improved edge detection method for interpreting gravity potential field data 认领 引用
1
作者 ZhiXin Xue Lei Jing +6 位作者 YaBin Yang MengLong Xu LongJun Qiu ChengYe Sun SuLi Shi ZhenNing Su Jian Fang 《Earth and Planetary Physics》 EI CSCD 2026年第3期497-511,共15页
Edge detection represents a critical task in potential field data interpretation and is extensively utilized for detecting faults, contacts, and other linear geological structures. However, conventional methods are co... Edge detection represents a critical task in potential field data interpretation and is extensively utilized for detecting faults, contacts, and other linear geological structures. However, conventional methods are constrained by several limitations, including inadequate balancing of signals with varying amplitude intensities, dispersed detection results, and insufficient suppression of spurious signals. To overcome these challenges, we propose an improved edge detection method, designated as the hyperbolic tangent (TANH) function with Gaussian envelope constraints on the total gradient modulus tilt angle (THASTG). The THASTG method is formally defined as a tilt angle approach based on the total gradient modulus, incorporating dual constraints through a Gaussian envelope and a TANH function. We initially conducted comparative analyses between the THASTG and established methods by using complex models simulating three distinct geological scenarios, thereby validating the feasibility of the methodology. Subsequent application to real gravity data from the South China Sea region demonstrated that compared with conventional techniques, THASTG yields enhanced structural detail, improved boundary resolution, and superior noise suppression. This method effectively suppresses noise interference and successfully avoids the introduction of spurious boundaries while maintaining consistency with previously documented major tectonic features. This study provides high-resolution structural constraints for the South China Sea region, delineates the offshore extension of the Red River Fault system, and accurately maps the continent-ocean boundary configuration. Our results demonstrate that the proposed methodology provides an effective tool for precise structural characterization and in-depth analysis of geodynamic evolution processes. 展开更多
关键词 edge detection potential field data gravity anomaly South China Sea
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PIDINet-MC:Real-Time Multi-Class Edge Detection with PiDiNet 认领 引用
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作者 Mingming Huang Yunfan Ye Zhiping Cai 《Computers, Materials & Continua》 SCIE EI 2026年第2期1983-1999,共17页
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e... As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time. 展开更多
关键词 Multi-class edge detection real-time lightweight deep supervision
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Bayesian-based ant colony optimization algorithm for edge detection 认领 引用
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作者 YU Yongbin ZHONG Yuanjingyang +6 位作者 FENG Xiao WANG Xiangxiang FAVOUR Ekong ZHOU Chen CHENG Man WANG Hao WANG Jingya 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期892-902,共11页
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
关键词 ant colony optimization(ACO) Bayesian algorithm edge detection transfer function.
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Edge detection in the potential field using the correlation coefficients of multidirectional standard deviations 认领 引用 被引量：11
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作者 徐梦龙 杨长保 +2 位作者 吴燕冈 陈竞一 郇恒飞 《Applied Geophysics》 SCIE CSCD 2015年第1期23-34,120,121,共12页
Most edge-detection methods rely on calculating gradient derivatives of the potential field, a process that is easily affected by noise and is therefore of low stability. We propose a new edge-detection method named c... Most edge-detection methods rely on calculating gradient derivatives of the potential field, a process that is easily affected by noise and is therefore of low stability. We propose a new edge-detection method named correlation coefficient of multidirectional standard deviations（CCMS） that is solely based on statistics. First, we prove the reliability of the proposed method using a single model and then a combination of models. The proposed method is evaluated by comparing the results with those obtained by other edge-detection methods. The CCMS method offers outstanding recognition, retains the sharpness of details, and has low sensitivity to noise. We also applied the CCMS method to Bouguer anomaly data of a potash deposit in Laos. The applicability of the CCMS method is shown by comparing the inferred tectonic framework to that inferred from remote sensing（RS） data. 展开更多
关键词 Edge detection Correlation coefficient multidirectional standard deviation Bouguer anomaly
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Memristive network-based genetic algorithm and its application to image edge detection 认领 引用 被引量：9
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作者 YU Yongbin YANG Chenyu +3 位作者 DENG Quanxin NYIMA Tashi LIANG Shouyi ZHOU Chen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第5期1062-1070,共9页
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
关键词 memristive network(MN) genetic algorithm(GA) edge detection mem-computing
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Edge Detection of River in SAR Image Based on Contourlet Modulus Maxima and Improved Mathematical Morphology 认领 引用 被引量：9
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作者 吴一全 朱丽 +2 位作者 郝亚冰 李立 卢文平 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期478-483,共6页
To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed b... To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology.The SAR image is firstly transformed to a contourlet domain.According to the directional information and gradient information of directional subband of contourlet transform,the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges,respectively.Subsequently,the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image.Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges,compared with the Canny method,the method based on wavelet and Canny,the method based on contourlet modulus maxima,and the method based on improved(ROEWA).The obtained river edges are complete and clear. 展开更多
关键词 synthetic aperture radar(SAR) image river detection edge detection contourlet transform modulus maxima
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An Image Edge Detection Algorithm Based on Multi-Feature Fusion 认领 引用 被引量：6
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作者 Zhenzhou Wang Kangyang Li +1 位作者 Xiang Wang Antonio Lee 《Computers, Materials & Continua》 SCIE EI 2022年第12期4995-5009,共15页
Edge detection is one of the core steps of image processing and computer vision.Accurate and fine image edge will make further target detection and semantic segmentation more effective.Holistically-Nested edge detecti... Edge detection is one of the core steps of image processing and computer vision.Accurate and fine image edge will make further target detection and semantic segmentation more effective.Holistically-Nested edge detection(HED)edge detection network has been proved to be a deep-learning network with better performance for edge detection.However,it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification.There will be detected edge incomplete,not smooth and other problems.To solve these problems,an image edge detection algorithm based on improved HED and feature fusion is proposed.On the one hand,features are extracted using the improved HED network:the HED convolution layer is improved.The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes.Meanwhile,the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information.On the other hand,edges are extracted using Otsu algorithm:Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value.Finally,the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge.Experimental results show that on the Berkeley University Data Set(BSDS500)the optimal data set size(ODS)F-measure of the proposed algorithm is 0.793;the average precision(AP)of the algorithm is 0.849;detection speed can reach more than 25 frames per second(FPS),which confirms the effectiveness of the proposed method. 展开更多
关键词 Computer vision edge detection deep learning feature fusion residual convolution
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Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection 认领 引用 被引量：4
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作者 YU Yongbin TANG Haowen +2 位作者 FENG Xiao WANG Xiangxiang HUANG Hang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2023年第3期641-649,共9页
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
关键词 edge detection figure of merit(FOM) memristor crossbar synaptic circuit memristor crossbar-based cellular neural network(MCM-CNN)
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Normalized edge detection, and the horizontal extent and depth of geophysical anomalies 认领 引用 被引量：4
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作者 李丽丽 韩立国 黄大年 《Applied Geophysics》 SCIE CSCD 2014年第2期149-157,252,253,共9页
Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies... Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies. Large deviations between model and true edges are common because of the interference of depth and errors in computing the derivatives; thus, edge detection methods cannot provide information about the depth of the source. To simultaneously obtain the horizontal extent and depth of geophysical anomalies, we use normalized edge detection filters, which normalize the edge detection function at different depths, and the maxima that correspond to the location of the source. The errors between model and actual edges are minimized as the depth of the source decreases and the normalized edge detection method recognizes the extent of the source based on the maxima, allowing for reliable model results. We demonstrate the applicability of the normalized edge detection filters in defining the horizontal extent and depth using synthetic and actual aeromagnetic data. 展开更多
关键词 geophysical anomalies normalized edge detection normalized total horizontal derivative regularization tilt angle theta map
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Edge detection method for remote sensing image based on morphological variable structuring element 认领 引用 被引量：4
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作者 YAO Li juan WANG Xiao peng +1 位作者 WANG Wei MA Wen gang 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第3期233-240,共8页
There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge ... There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge detection method for remote sensing image based on variable structuring element is proposed. Firstly, the structuring elements with different scales and multiple directions are constructed according to the diversity of remote sensing imagery targets. In order to suppress the noise of the target background and highlight the edge of the image target in the remote sensing image by adaptive Top hat and Bottom hat transform, the corresponding adaptive morphological operations are constructed based on variable structuring elements; Secondly, adaptive morphological edge detection is used to obtain multiple images with different scales and directional edge features; Finally, the image edges are obtained by weighted summation of each direction edge, and then the least square is used to fit the edges for accurate location of the edge contour of the target. The experimental results show that the proposed method not only can detect the complete edge of remote sensing image, but also has high edge detection accuracy and superior anti-noise performance. Compared with classical edge detection and the morphological edge detection with a fixed single structuring element, the proposed method performs better in edge detection effect, and the accuracy of detection can reach 95 % 展开更多
关键词 edge detection remote sensing image variable structuring element least squares method
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Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold 认领 引用 被引量：2
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作者 Shouming Hou Chaolan Jia +3 位作者 Kai Li Liya Fan Jincheng Guo Mackenzie Brown 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第7期81-94,共14页
Edge detection is an effective method for image segmentation and feature extraction.Therefore,extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019(COVID-19)CT images is extremely important.Mu... Edge detection is an effective method for image segmentation and feature extraction.Therefore,extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019(COVID-19)CT images is extremely important.Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy.In this paper,we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex(GF_SSR),and improved multiscale morphology and adaptive threshold binarization(IMSM_ATB).As all the CT images have noise,we propose to remove image noise by Gaussian filtering.The edge of CT images is enhanced using the SSR algorithm.In addition,based on the extracted edge of CT images using improved Multiscale morphology,a particle swarm optimization(PSO)algorithm is introduced to binarize the image by automatically getting the optimal threshold.To evaluate our method,we use images from three datasets,namely COVID-19,Kaggle-COVID-19,and COVID-Chestxray,respectively.The average values of results are worthy of reference,with the Shannon information entropy of 1.8539,the Precision of 0.9992,the Recall of 0.8224,the F-Score of 1.9158,running time of 11.3000.Finally,three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm.Compared with the other four algorithms,the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction. 展开更多
关键词 COVID-19 SSR multiscale morphology PSO adaptive threshold edge detection
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Edge detection of molten pool and weld line for CO_2 welding based on B-spline wavelet 认领 引用 被引量：3
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作者 薛家祥 贾林 +1 位作者 李海宝 张丽玲 《China Welding》 EI CAS 2004年第2期137-141,共5页
Due to the disturbances of spatters, dusts and strong arc light, it is difficult to detect the molten pool edge and the weld line location in CO_2 welding processes. The median filtering and self-multiplication was em... Due to the disturbances of spatters, dusts and strong arc light, it is difficult to detect the molten pool edge and the weld line location in CO_2 welding processes. The median filtering and self-multiplication was employed to preprocess the image of the CO_2 welding in order to detect effectively the edge of molten pool and the location of weld line. The B-spline wavelet algorithm has been investigated, the influence of different scales and thresholds on the results of the edge detection have been compared and analyzed. The experimental results show that better performance to extract the edge of the molten pool and the location of weld line can be obtained by using the B-spline wavelet transform. The proposed edge detection approach can be further applied to the control of molten depth and the seam tracking. 展开更多
关键词 CO_2 welding molten pool B-spline wavelet edge detection
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Edge detection of magnetic tile cracks,based on wavelet 认领 引用 被引量：2
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作者 林丽君 He Mingge +2 位作者 Yin Ying Yin Xiangyun Yin Guofu 《High Technology Letters》 EI CAS 2015年第3期276-281,共6页
In order to extract the defect edge information on the magnetic tile surface with low contrast and textured background,an edge detection algorithm based on image weighted information entropy and wavelet modulus maxima... In order to extract the defect edge information on the magnetic tile surface with low contrast and textured background,an edge detection algorithm based on image weighted information entropy and wavelet modulus maxima is proposed.At first,a new Butterworth high pass filter(BHPF) with adaptive cutoff frequency is produced,because the clarity and complexity of the textured background are described by the weighted information entropy of the image gradient variance quantitatively,and the filter can change its parameters through matching the non-linear relationship between the information entropy and the cutoff frequency.And then,the best decomposition scale is obtained by the level determination function to prevent edge information from missing.At last,edge points are got by double threshold after obtaining the wavelet modulus maxima,and then the edge image is linked by the edge points to ensure the edge continuity and veracity.Experiment results indicate that the proposed algorithm outperforms the conventional Canny and Sobel algorithm,and the edge detection algorithm can also detect other defects,and lays the foundation for defecting auto- recognition. 展开更多
关键词 edge detection wavelet transform textures processing magnetic tile informationentropy
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SAR image despeckling based on edge detection and nonsubsampled second generation bandelets 认领 引用 被引量：3
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作者 Zhang Wenge Liu Fang +1 位作者 Jiao Licheng Gao Xinbo 《Journal of Systems Engineering and Electronics》 SCIE EI 2009年第3期519-526,共8页
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)... 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
关键词 computer image processing synthetic aperture radar speckle edge detection nonsubsampled second generation bandelet transform Canny operator threshold shrinkage.
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Adaptive Dual-Threshold Edge Detection Based on Wavelet Transform 认领 引用 被引量：3
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作者 侯舒娟 梅文博 张志明 《Journal of Beijing Institute of Technology》 EI CAS 2003年第3期247-250,共4页
In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detectio... In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detection algorithm is proposed. The local maximum modulus is extracted by linear interpolation in wavelet domain. With the analysis on histogram, the image is filtered with an adaptive dual threshold method, which effectively detects the contours of small structures as well as the boundaries of large objects. A wavelet domain's propagation function is used to further select weak edges. Experimental results have shown the self adaptivity of the threshold to images having the same kind of histogram, and the efficiency even in noise tampered images. 展开更多
关键词 wavelet transform edge detection propagation function dual threshold
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A new fuzzy edge detection algorithm 认领 引用 被引量：6
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作者 孙伟 夏良正 《Journal of Southeast University(English Edition)》 EI CAS 2003年第2期132-135,共4页
Based upon the maximum entropy theorem of information theory, a novel fuzzy approach for edge detection is presented. Firstly, a definition of fuzzy partition entropy is proposed after introducing the concepts of fuzz... Based upon the maximum entropy theorem of information theory, a novel fuzzy approach for edge detection is presented. Firstly, a definition of fuzzy partition entropy is proposed after introducing the concepts of fuzzy probability and fuzzy partition. The relation of the probability partition and the fuzzy c-partition of the image gradient are used in the algorithm. Secondly, based on the conditional probabilities and the fuzzy partition, the optimal thresholding is searched adaptively through the maximum fuzzy entropy principle, and then the edge image is obtained. Lastly, an edge-enhancing procedure is executed on the edge image. The experimental results show that the proposed approach performs well. 展开更多
关键词 edge detection fuzzy entropy image segmentation fuzzy partition
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Robust edge detection based on stationary wavelet transform 认领 引用 被引量：6
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作者 章国宝 刘泉 《Journal of Southeast University(English Edition)》 EI CAS 2006年第2期218-221,共4页
By combining multiscale stationary wavelet analysis with fuzzy c-means, a robust edge detection algorithm is presented. Based on the translation invafiance built in multiscale stationary wavelet transform, components ... By combining multiscale stationary wavelet analysis with fuzzy c-means, a robust edge detection algorithm is presented. Based on the translation invafiance built in multiscale stationary wavelet transform, components in different transformed sub-images corresponding to a pixel are employed to form a feature vector of the pixel. All the feature vectors are classified with unsupervised fuzzy c-means to segment the image, and then the edge pixels are checked out by the Canny detector. A series of images contaminated with different intensive Gaussian noises are used to test the novel algorithm. Experiments show that fairly precise edges can be checked out robustly from those images with fairly intensive noise by the proposed algorithm. 展开更多
关键词 edge detection stationary wavelet multiscale analysis fuzzy c-means
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Edge detection and mathematic fitting for corneal surface with Matlab software 认领 引用 被引量：9
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作者 Yue Di Mei-Yan Li +1 位作者 Tong Qiao Na Lu 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2017年第3期336-342,共7页
AIM：To select the optimal edge detection methods to identify the corneal surface,and compare three fitting curve equations with Matlab software. METHODS：Fifteen subjects were recruited. The corneal images from optic... AIM：To select the optimal edge detection methods to identify the corneal surface,and compare three fitting curve equations with Matlab software. METHODS：Fifteen subjects were recruited. The corneal images from optical coherence tomography（OCT）were imported into Matlab software. Five edge detection methods（Canny,Log,Prewitt,Roberts,Sobel）were used to identify the corneal surface. Then two manual identifying methods（ginput and getpts）were applied to identify the edge coordinates respectively. The differences among these methods were compared. Binomial curve（y=Ax2＋Bx＋C）,Polynomial curve [p（x）=p1xn＋p2x（n-1）＋....＋pnx＋pn＋1] and Conic section（Ax2＋Bxy＋Cy2＋Dx＋Ey＋F=0）were used for curve fitting the corneal surface respectively. The relative merits among three fitting curves were analyzed. Finally,the eccentricity（e）obtained by corneal topography and conic section were compared with paired t-test. RESULTS：Five edge detection algorithms all had continuous coordinates which indicated the edge of the corneal surface. The ordinates of manual identifying were close to the inside of the actual edges. Binomial curve was greatly affected by tilt angle. Polynomial curve was lack of geometrical properties and unstable. Conic section could calculate the tilted symmetry axis,eccentricity,circle center,etc. There were no significant differences between ＇e＇ values by corneal topography and conic section（t=0.9143,P=0.3760 〉0.05）.CONCLUSION：It is feasible to simulate the corneal surface with mathematical curve with Matlab software. Edge detection has better repeatability and higher efficiency. The manual identifying approach is an indispensable complement for detection. Polynomial and conic section are both the alternative methods for corneal curve fitting. Conic curve was the optimal choice based on the specific geometrical properties. 展开更多
关键词 Matlab software edge detection curve fitting mathematic simulation optical coherence tomography corneal topography
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Feature fusion method for edge detection of color images 认领 引用 被引量：5
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作者 Ma Yu Gu Xiaodong Wang Yuanyuan 《Journal of Systems Engineering and Electronics》 SCIE EI 2009年第2期394-399,共6页
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... 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
关键词 color image processing edge detection feature extraction feature fusion
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Edge detection based on object tree image representation and wavelet transform 认领 引用 被引量：2
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作者 屈彦呈 王常虹 庄显义 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2003年第1期68-71,共4页
In applications such as image retrieval and recognition, precise edge detection for interested regions plays a decisive role. Existing methods generally take little care about local characteristics, or become time con... In applications such as image retrieval and recognition, precise edge detection for interested regions plays a decisive role. Existing methods generally take little care about local characteristics, or become time consuming if every detail is considered. In the paper, a new method is put forward based on the combination of effective image representation and multiscale wavelet analysis. A new object tree image representation is introduced. Then a series of object trees are constructed based on wavelet transform modulus maxima at different scales in descending order. Computation is only needed for interested regions. Implementation steps are also given with an illustrative example. 展开更多
关键词 Edge detection object tree wavelet transform modulus maxima scale
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