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SAR image edge detection With Recurrent Guidance Filter

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2021年第6期18卷 1064-1068页

作者： Jing, Wenbo Jin, Tian Xiang, Deliang Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Sci & Technol Near Surface Detect Lab Wuxi 214035 Jiangsu Peoples R China Natl Innovat Inst Technol Beijing 100091 Peoples R China

While traditional edge detectors concentrate on modifying the shape of the window function, we consider the edge detection problem from a new perspective, and an effective recurrent guidance filter is proposed in this letter. The proposed filter is elaborately designed for edge detection tasks and aims to remove the nonedge information including speckle noise and detailed texture and preserve edge information simultaneously. We first filter the image by the proposed filter and a filtered image is obtained. Then, by using the edge detector with the Gaussian-shaped window, which was previously proposed by us and performing the postprocessing method, the edge response is extracted from the filtered image. Both objective and subjective experimental results on simulated and real synthetic aperture radar (SAR) images demonstrate that the edge detector based on the recurrent guidance filter yields better performance than the state-of-the-art edge detectors.

关键词： image edge detection Detectors Microsoft Windows Synthetic aperture radar Speckle Shape Radar imaging edge detection ratio-based edge detector recurrent guidance filter synthetic aperture radar (SAR) image

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image edge detection based on nonsubsampled contourlet transform and mathematical morphology

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Journal of Southeast University(English Edition) 2012年第4期28卷 445-450页

作者：何坤贤王庆肖彦昌王晓兵东南大学仪器科学与工程学院南京210096 南京炮兵学院射击教研室南京211132

A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled contourlet transform into multi-scale and multi-directional subbands. Then the edges in the high-frequency and low-frequency sub-bands are respectively extracted by the dualthreshold modulus maxima method and the mathematical morphology operator. Finally, the edges from the high- frequency and low-frequency sub-bands are integrated to the edges of the source image, which are refined, and isolated points are excluded to achieve the edges of the source image. The simulation results show that the proposed algorithm can effectively suppress noise, eliminate pseudo-edges and overcome the adverse effects caused by uneven illumination to a certain extent. Compared with the traditional methods such as LoG, Sobel, and Carmy operators and the modulus maxima algorithm, the proposed method can maintain sufficient positioning accuracy and edge details, and it can also make an improvement in the completeness, smoothness and clearness of the outline.

关键词： image edge detection nonsubsampled contourlet transform ( NSCT ) modulus maxima dual-threshold mathematical morphology structural elements

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Adaptive Neural Network for image edge detection

Adaptive Neural Network for Image Edge Detection

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1st International Conference on Energy and Environmental Protection (ICEEP 2012)

作者： Zhang, Weitang Huang, Shaogang Hefei Univ Dept Elect Engn Hefei 230022 Anhui Peoples R China Nanchang Univ Sch Elect Engn & Automat Nanchang 330031 Jiangxi Peoples R China

ISBN: (纸本)9783037854174

This paper presents a neural network adaptive image edge detection method, and from neural network theory, this paper gives the formula of adaptive neural network algorithm;quantitative given the momentum factor and error, momentum factor and error on the weight vector of norm of the gradient of the quantitative relationship;and gives the algorithm flow diagram. Through experiment we get the conclusion: by using this adaptive neural network for image edge detection is feasible, and it has good generalization ability.

关键词： Adaptive neural network image edge detection image segmentation

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Gray-scale image edge detection Based on Order Morphology Transformation

Gray-scale Image Edge Detection Based on Order Morphology Tr...

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7th World Congress on Intelligent Control and Automation

作者： Xu, Yanlei Zhao, Jiyin Jiao, Yubin Jilin Univ Sch Commun Engn Changchun 130022 Peoples R China Jilin Agr Univ Coll Informat & Technol Changchun 130118 Peoples R China Jilin Univ DaLian Nationalities Univ Sch Commun Engn Coll Electromech & Informat Engn Changchun 130022 Peoples R China Armored Forces Technol Inst Dept Automat Changchun 130117 Peoples R China

ISBN: (纸本)9781424421138

This paper constructs three edge detection operations after studying the order morphological transformation deeply. The specialities of this operations and choices of the structure element are analyzed, so a new multi-scale and multi-structuring edge detection operations based on the three former operations are proposed. The operations can obtain clear and exact edge of the noisy images. By simulation and comparing with the Sobel, Canny and traditional order morphology operations, the operations are more effective on noise restraining and retaining the image details.

关键词： image edge detection Order morphological transformation Structure element Multi-scale and multi-structuring Noise restraining

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A novel image edge detection using fractal compression

A novel image edge detection using fractal compression

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11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks

作者： Zhang, Liangbin Xi, Lifeng Zhejiang Wanli Univ Sch Comp Sci & Informat Technol Ningbo 315100 Zhejiang Peoples R China

ISBN: (纸本)9783540748267

image edges are the foundation of image texture and shape figure extraction. In this paper we propose a novel edge detection method based on the self-similarity of fractal compression. We point out that the mean-square-error distance (MSE) of fractal compression can be used to extract edge of fractal image effectively. The self-similarity coefficient between the local range block and the searching domain block is centered at the current pixel being processed, and near-center self-affine transform is applied in local searching process, finally a binary operator is used to threshold its magnitude and produce the edge map of the image. The results of experiments show that the proposed new algorithm for image edge detection is valid and effective. It also has good antinoise performance.

关键词： image edge detection fractal coding MSE self-similarity.

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An Improved Method for image edge detection Based on GM(1,1) Model

An Improved Method for Image Edge Detection Based on GM(1,1)...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Zhang, Jianxin Wu, Lang Zhejiang Sci Tech Univ Coll Mech Engn & Automat Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9780769538167

The GM(1,1) model usually uses the first component of the first order accumulated generating operation(AGO) sequence X-(1) as the initial condition to model in the research and application of image edge detection. This model has the disadvantage of being unable to completely utilize the latest information. Also it can't detect out all edge points because it detects the image only on two directions of horizontal and vertical. So in this paper, an improved GM(1,1) model is proposed for edge detection. It uses the last component of sequence X-(1) as the initial condition of GM(1,1) model and predicts the pixel value on eight directions. Then the predicted image is subtracted by the original image to find out the position points with hopping grey values, which are edge points. Experimental results show that this algorithm can effectively detect out a complete image edge.

关键词： image edge detection GM(1,1) model grey system theory initial condition

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A Novel image edge detection Method Based on Multi-Population Ant Colony Optimization 6

A Novel Image Edge Detection Method Based on Multi-Populatio...

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6th International Conference on Information Science and Control Engineering (ICISCE)

作者： Wang, Shu Jilin Univ Zhuhai Coll Coll Comp Sci Zhuhai 519041 Peoples R China

ISBN: (纸本)9781728157122

edge detection is a fundamental problem in image processing and computer vision. Recently ant colony optimization (ACO) algorithm has been used in edge detection, most existing ACO-based edge detection methods used single ant population and a fixed number of neighborhood pixels to calculate the gradient in the heuristic information for each pixel in transition probability. Those make the algorithm tend to obtain local optima. To enhance the accuracy of the ACO-based edge detection methods, a multiple-population strategy is utilized in this paper. The artificial ants are divided into two populations to obtain both advantages of global search and local search, one trends to make the ants move around optima to focus on local search, the other population make the ants move sharply from current position to jump out local optima and explore global optima. The experimental results show the effectiveness of the proposed method.

关键词： computer vision Ant Colony Optimization image edge detection

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An Approach Towards image edge detection Based on Interval-Valued Fuzzy Mathematical Morphology and Admissible Orders 11

An Approach Towards Image Edge Detection Based on Interval-V...

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11th Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT)

作者： Sussner, Peter Carazas, Lisbeth Corbacho Univ Estadual Campinas Dept Appl Math IMECC Campinas SP Brazil

ISBN: (纸本)9789462527706

edge detection is an important step for preprocessing digital images before more advanced methods of image analysis such as segmentation can be applied. There are an infinite number of edge detectors that can be derived from pairs of fuzzy dilation and erosion operators. Usually, an edge detector is based on the incorrect assumption that there is no uncertainty regarding the pixel values of the given digital image. The approaches presented in this paper do not rely on this assumption. Instead, the uncertainty regarding the pixel values is modelled in terms of an interval-valued image. After an application of an interval-valued fuzzy dilation and erosion, we are able to produce a binary edge image after a number of steps including an order-preserving transformation based on an admissible order.

关键词： image edge detection Interval-valued fuzzy mathematical morphology Morphological gradient Admissible order

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Two-Layered Oscillatory Neural Networks with Analog Feedforward Majority Gate for image edge detection Application 56

Two-Layered Oscillatory Neural Networks with Analog Feedforw...

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56th IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Abernot, Madeleine Delacour, Corentin Suna, Ahmet Gregg, J. Marty Karg, Siegfried Todri-Sanial, Aida Univ Montpellier CNRS LIRMM Montpellier France Queens Univ Belfast Sch Math & Phys Belfast Antrim North Ireland IBM Res Europe Dept Sci & Technol Zurich Switzerland Eindhoven Tech Univ Dept Elect Engn Eindhoven Netherlands

ISBN: (纸本)9781665451093

The increasing volume of smart edge devices, like smart cameras, and the growing amount of data to treat incited the development of light edge Artificial Intelligence (AI) solutions with neuromorphic computing. Oscillatory Neural Network (ONN) is a promising neuromorphic computing approach which uses networks of coupled oscillators, and their inherent parallel synchronization to compute. Also, ONN phase computing allows to limit voltage amplitude and reduce power consumption. Low-power, fast, and parallel computation properties make ONN attractive for edge AI. In state-of-the-art, ONN is built with a fully-connected architecture, with coupling defined from un-supervised learning to perform auto-associative memory tasks, like with Hopfield Networks. However, to allow ONN to solve beyond associative memory applications, there is a need to explore further ONN architectures. In this work, we propose a novel architecture of cascaded analog fully-connected ONNs interconnected with an analog feedforward majority gate layer. In particular, we show this architecture can solve image edge detection task using two fully-connected ONN layers. This is, to our best knowledge, a first analog-based solution to cascade two fully-connected ONNs.

关键词： Oscillatory Neural Networks image edge detection Majority Gate Feedforward

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The Application of image edge detection by using fuzzy technique

The Application of image edge detection by using fuzzy techn...

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Conference on Electronic Imaging and Multimedia Technology IV

作者： Li, YH Liu, BQ Bin, Z Ordnance Engn Coll Dept Opt & Elect Engn Hebei Shijiazhuang 050003 Peoples R China

ISBN: (纸本)081945592X

A new method to deal with images by computer is put forward, which is more convenient for the eyes to identify and much easier to understand. As the edge is a basic feature of image, checking it is one of the most important parts in processing image. The traditional technique is to use the edge detection partial operator, which is to detect the gray level changes of neighbors of every pixel, and to detect the edge by using the changing regular of one-order or two-order directional differential coefficient. But sometimes there is uncertainty of the edge, and man can't distinguish whether it is the edge or not. In order to turn the fuzzy edge to be clear and solve the problem above, this paper mentions fuzzy enhancement to realize image edge detection. Fuzzy technology is a newly rising technology used in many fields, especially in the image processing, and fuzzy enhancement is one important part of the fuzzy technology. Based on this technology, this paper firstly sets the image fuzzy feature plane of the original image, secondly proceeds the fuzzy enhancement, and then detects the edge by Sobel differential arithmetic. At the end of the paper, it realizes the histogram algorithm and the fuzzy enhancing algorithm by Visual C++. Results of the experiment show that fuzzy enhancement is superior in image processing.

关键词： fuzzy enhancing histogram enhancing image edge detection

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