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canny edge detector improvement using an intelligent ants routing

EVOLVING SYSTEMS 2021年第2期12卷 397-406页

作者： Benhamza, Karima Seridi, Hamid Guelma Univ LabSTIC 08 Mai 1945POB 401 Guelma Algeria

canny operator provides simple technique to extract the useful edge information from image. However, it suffers from two problems: a difficulty to choose the thresholds values and the presence of broken edges. This paper proposes to improve canny detector in two steps: First, adaptive Otsu threshold is used to select appropriate thresholds. Then, a new variant of ant colony optimization (ACO) algorithm is used to recover missing edges. From binary image, extracted edge endpoints are used as starting pixels for an intelligent ants routing mechanism. Ants are directed to suitable contour areas and missing edges are retraced via the pheromone traces. Proposed improvements are evaluated with entropy and kappa methods. Experimental results are good and assert approach's ability to recover most of the broken edges, particularly in noisy clinical images.

关键词： canny algorithm Adaptive thresholding Endpoints ACO routing process

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Efficacy of Gaussian smoothing in canny edge detector

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ELECTRONICS LETTERS 2000年第19期36卷 1615-1617页

作者： Cope, RK Rockett, PI Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD S Yorkshire England

The authors examine the efficacy of the noise-filtering stage in the canny edge detector using the receiver operating characteristic paradigm and find that omitting the filtering stage can give superior results. They ... 详细信息

关键词： Gaussian smoothing filtering theory Filtering methods in signal processing edge detection smoothing methods canny algorithm receiver operating characteristics noise filtering Image recognition edge detector

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An Improved canny Edge Detector and its Realization on FPGA

An Improved Canny Edge Detector and its Realization on FPGA

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7th World Congress on Intelligent Control and Automation

作者： He, Wenhao Yuan, Kui Chinese Acad Sci Hi Tech Innovat Ctr Inst Automat Beijing 100083 Peoples R China

ISBN: (纸本)9781424421138

canny edge detector treats edge detection as a signal processing problem to design an optimal edge detector and has been widely used for edge detection. However, the traditional canny edge detector has two shortcomings. First, the threshold of the algorithm needs to be set by manual. Secondly, the algorithm is very time consuming and can not be implemented in real time. A new self-adapt threshold canny algorithm is proposed in this paper to solve the first problem. A pipelined implementation on FPGA for this new algorithm is also designed to solve the second problem. Experiment results are also given to show the efficiency of the proposed method.

关键词： canny algorithm self-adapt threshold FPGA

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Edge-preserving image denoising using a deep convolutional neural network

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SIGNAL PROCESSING 2019年 159卷 20-32页

作者： Shandoosti, Hamid Reza Rahemi, Zahra Hamedan Univ Technol Dept Elect Engn Hamadan 65155 Iran

This paper introduces a novel denoising approach making use of a deep convolutional neural network to preserve image edges. The network is trained by using the edge map obtained from the well-known canny algorithm and aims at determining if a noisy patch in non-subsampled shearlet domain corresponds to the location of an edge. In the first step of the proposed denoising algorithm, we use the nonsubsampled shearlet transform to decompose the noisy image into a low-frequency subband and a series of high-frequency subbands. Subsequently, 3D blocks are formed by stacking 2D blocks of high-frequency subbands along a specific direction. Each 3D patch is then fed to the trained deep convolutional neural network to determine if it belongs to the edge-related class or not. Finally, the NSST (non-subsampled shearlet transform) coefficients belonging to the edge-related class remain unchanged, and those not belonging to the edge-related class are denoised by the shrinkage method using an adaptive threshold. Experimental results on various test images including benchmark grayscale images and medical ultrasound images demonstrate that the proposed method achieves better performance compared to some state-of-the-art denoising approaches. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Image denoising Deep convolutional neural network Non-subsampled shearlet transform canny algorithm Adaptive threshold

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Rapid contour detection for image classification

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IET IMAGE PROCESSING 2018年第4期12卷 532-538页

作者： Rasche, Christoph Univ Politehn Bucuresti Image Proc & Anal Lab Bucharest Romania

The author introduces a contour detection method that has relatively low complexity yet still highly accurate. The method is based on extrema detection along the four principal orientations, a trick that can be used to detect not only edges but, in particular, also ridges and rivers. The author makes a comparison to the popular canny algorithm and shows that the proposed method's only downside is that it cannot detect very high curvatures in edge contours. The method is applied to the task of image classification (satellite images, Caltech-101, etc.) and it is demonstrated that the use of all three contour types (edges, ridges, and rives) improves classification accuracy as opposed to the use of only edge contours. Thus, for image classification, it is more important to extract multiple contour features;the use of the exact detection method appears to play a smaller role. The author's simple method is also appealing for use in individual frames, due to its low complexity.

关键词： edge detection feature extraction image classification rapid contour detection method image classification canny algorithm contour feature extraction

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An Intelligent Evaluation Method of Supply-Demand Matching Degree Based on ELECTRE III and VIKOR in Cloud Manufacturing Platform

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IEEE ACCESS 2024年 12卷 72317-72336页

作者： Pang, Jihong Shi, Yuxuan Dai, Jinkun Shaoxing Univ Coll Business Shaoxing 312000 Peoples R China Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China Zhejiang Coll Secur Technol Coll New Energy Equipment Wenzhou 325000 Peoples R China

Supply-demand matching degree evaluation is an effective way to reduce the transaction risk of manufacturing supply-demand subjects. Cloud manufacturing provides a platform for supply-demand entities through the network. Based on the characteristics of cloud manufacturing, this paper proposes an intelligent evaluation method of supply-demand matching degree based on ELECTRE III and VIKOR. Firstly, the demand side uploads a bill of material on the cloud manufacturing platform, and the supplier provides a supply plan. The demand side forms an evaluation team to evaluate the quality of the supplied product. Secondly, the weight of the evaluators is calculated in cloud manufacturing platform based on the similarity measurement method. Then, the cloud manufacturing platform carries out the weighted aggregation of evaluation information and uses the ELECTRE III method to build the weak advantage relationship among indicators. Finally, the cloud manufacturing platform performs a VIKOR compromise calculation and selects the optimal supply-demand matching degree. At the end of the paper, an example is taken to evaluate the matching degree of supply-demand of robot machined parts, to demonstrate the practicability of the new method. Based on canny algorithm, a robot action stability test platform is built to verify the calculation results of the new method.

关键词： Manufacturing Cloud computing Resource management Production Rough sets Costs Clustering algorithms Supply and demand canny algorithm cloud manufacturing platform ELECTRE III supply-demand matching degree evaluation VIKOR

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Plasma Shape Identification and Fitting on the Basis of Image Processing on Tokamak

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JOURNAL OF FUSION ENERGY 2015年第6期34卷 1348-1355页

作者： Xue, Erbing Zhang, Xianmei Luo, Jiarong Yu, Limin E China Univ Sci & Technol Sch Sci Dept Phys Shanghai 200237 Peoples R China Donghua Univ Coll Sci Dept Phys Shanghai 201620 Peoples R China

To perform experiments and data evaluation in tokamak devices, plasma shape identification is needed for plasma equilibrium control and analysis. A new framework for plasma shape identification is currently being developed in EAST. The foundations of the system are high speed CCD, the remote computer and the algorithms for plasma boundary identification and fitting. In this paper, a new quasi-real time method for plasma shape identification, based on the modified canny algorithm and carried out in EAST, is proposed. Comparison with the result of EFIT and other diagnostic data, our method has not only been verified the stability and dependability of the detection method, but also revealed it being a reliable tool for operators and physicists to analyze the behavior of plasma. The real time plasma shape identification can be introduced to the plasma control system in the coming campaign of EAST.

关键词： Image processing Boundary detection Fitting Tracking canny algorithm

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Intelligent crack extraction based on terrestrial laser scanning measurement

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MEASUREMENT & CONTROL 2020年第3-4期53卷 416-426页

作者： Yang, Hao Xu, Xiangyang Jiangsu Univ Sci & Technol Zhenjiang Jiangsu Peoples R China Leibniz Univ Hannover Fac Civil Engn & Geodet Sci Hannover Germany

The hazards of cracks, which could badly decrease reliability and safety of structures, are receiving increasing attention with the popularity of tunnel constructions. Traditional crack inspection relies on visual examination, which is time-, cost- and labor-intensive. Therefore, how to identify and measure cracks intelligently is significantly essential. The paper focuses on the canny method to extract cracks of tunnel structures by the intensity value of reflectivity. We propose and investigate a novel method which combines dilation and the canny algorithm to identify and extract the cracks automatically and intelligently based on the point cloud data of terrestrial laser scanning measurement. In order for measurement of cracks, the projection of summed edge pixels is adopted, where a synthesis is carried out on the detection results with all sampling parameters. Based on the synthesized image, vertical crack presents two sharp peaks where the space of the peaks indicates the average width of the crack, as well as its position. The advantage of the method is that it does not require determination of canny detector parameters. The deviation between manual measurement and canny detection is 2.92%.

关键词： TLS crack extraction tunnel structures canny algorithm intelligent identification

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Radar Determination of Fault Slip and Location in Partially Decorrelated Images

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PURE AND APPLIED GEOPHYSICS 2017年第6期174卷 2295-2310页

作者： Parker, Jay Glasscoe, Margaret Donnellan, Andrea Stough, Timothy Pierce, Marlon Wang, Jun CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA Indiana Univ 2709 East 10th St Bloomington IN 47408 USA

Faced with the challenge of thousands of frames of radar interferometric images, automated feature extraction promises to spur data understanding and highlight geophysically active land regions for further study. We have developed techniques for automatically determining surface fault slip and location using deformation images from the NASA Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), which is similar to satellite-based SAR but has more mission flexibility and higher resolution (pixels are approximately 7 m). This radar interferometry provides a highly sensitive method, clearly indicating faults slipping at levels of 10 mm or less. But interferometric images are subject to decorrelation between revisit times, creating spots of bad data in the image. Our method begins with freely available data products from the UAVSAR mission, chiefly unwrapped interferograms, coherence images, and flight metadata. The computer vision techniques we use assume no data gaps or holes;so a preliminary step detects and removes spots of bad data and fills these holes by interpolation and blurring. Detected and partially validated surface fractures from earthquake main shocks, aftershocks, and aseismic-induced slip are shown for faults in California, including El Mayor-Cucapah (M7.2, 2010), the Ocotillo aftershock (M5.7, 2010), and South Napa (M6.0, 2014). Aseismic slip is detected on the San Andreas Fault from the El Mayor-Cucapah earthquake, in regions of highly patterned partial decorrelation. Validation is performed by comparing slip estimates from two interferograms with published ground truth measurements.

关键词： Radar interferometry fault slip computer vision canny algorithm

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Image-based crop row detection utilizing the Hough transform and DBSCAN clustering analysis

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IET IMAGE PROCESSING 2024年第5期18卷 1161-1177页

作者： Zhao, Richeng Yuan, Xianju Yang, Zhanpeng Zhang, Lei Hubei Univ Automot Technol Sch Automot Engn Shiyan Peoples R China Hubei Key Lab Automot Power Transmiss & Elect Cont Shiyan Peoples R China Hubei Univ Automot Technol Sch Automot Engn Shiyan Peoples R China

More accurate methods for crop row detection benefit intelligent operation of agricultural machinery, especially avoiding mishandling or crushing crops. For achieving such a target, a traditional method combining the ExGR exponents, Otsu algorithm, canny method, Hough transform and DBSCAN clustering analysis is proposed so that centerlines of crop rows can be detected effectively without manual intervention. Specifically, ExGR exponents are first adopted to gray green plants. The threshold of binarization will be further obtained by the Otsu algorithm. Further adopting the edge detection algorithm (canny), edges of crops can be determined. Finally, combining the Hough transform and DBSCAN clustering analysis, the crop row detection is effectively available. Utilizing these methods, numerical simulation and their comparisons with existing methods are also achieved. For example, the canny algorithm is relatively accurate than the Suzuki algorithm as well as their combinations with a geometric center extraction method if the density of weed is high. Compared with the K-means clustering method, the DBSCAN algorithm is more suitable to characterize crop rows optimally in more complex conditions. It is validated from experiments that the combination of canny algorithm, Hough transform and DBSCAN clustering is better than other mentioned traditional methods. A traditional method combining the ExGR exponents, Otsu algorithm, canny method, Hough transform and DBSCAN clustering analysis is proposed so that centerlines of crop rows can be detected effectively. Such an improved method could be suggested to process complex images including green crops, weeds, clods and ***

关键词： Agricultural engineering canny algorithm crop row detection DBSCAN clustering ExGR index Hough transform image processing

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