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International Conference on computer and Communications (ICCC)

作者： Guangqi Liu Qinghai Ding Moran Ju Haibo Luo Tianming Jin Miao He The Key Laboratory of Image Understanding and Computer Vision Shenyang China Space Star Technology Company Ltd. Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

A novel filtering algorithm is proposed based on level set method (LSM) and linear time Euclidean distance transform (LET) algorithm in this paper, which has the property of shape retention and thus is suitable for post-processing of the initial contours for contacting instances in digital Pap image. As one of our contributions, we propose two new metrics based on the pixel-level average false positive rate and false negative rate that used by baseline method. A significant decrease in pixel-level average false positive rate (FP) by 62% can obtain by our proposed method. The result of quantitative and qualitative evaluation shows that our proposed shape retentive filtering algorithm (SRFA) can effectively filter out the false positive fragments of the initial instance contour of cervical cells from the ISBI-2014 dataset.

关键词： Measurement Shape Level set Transforms Euclidean distance Filtering algorithms Medical diagnostic imaging

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Graph Convolutional Neural Network with Inter-layer Cascade Based on Attention Mechanism 7

Graph Convolutional Neural Network with Inter-layer Cascade ...

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7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Wei, Lu Liu, Yiting Feng, Kaiyuan Li, Jianzhao Sheng, Kai Wu, Yue Xidian University Key Laboratory of Big Data and Intelligent Vision School of Computer Science and Technology Xi'an710071 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic Engineering Xi'an710071 China Academy of Advanced Interdisciplinary Research Xidian University Xi'an710071 China

ISBN: (纸本)9781665441490

In recent years, graph data in the non-Euclidean space has been widely used, and the methods and techniques for learning graph data in many deep learning fields have been continuously developed, such as the Graph neural network (GNN). The structural characteristics of graph data, the aggregation mode and representation mode of node information, and the node neighbor information are the core issues of GNN. However, most of the existing graph convolutional neural networks have an excessive smoothing problem, which limits the learning ability of the model. Aiming at the over-smoothing problem of the current algorithm, this paper enhances the learning of graph data by improving the expression ability of local information and global features. This paper constructs a cascade structure between graph convolutional layers. This kind of network structure realizes the dense connection of convolutional layers, makes the local feature information is effectively used, and further enhances the graph representation ability. Introduce self-attention and TopK into the Readout module, selectively aggregate and express feature information, and use graph-level information more efficiently. Graph classification is a downstream task to verify the performance of the proposed model. Experimental results prove that this densely structured graph convolutional network can effectively aggregate local node information and global graph-level information. © 2021 IEEE.

关键词： Graph neural networks

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Fletcher-reeves conjugate gradient for sparse reconstruction: Application to image compressed sensing

Fletcher-reeves conjugate gradient for sparse reconstruction...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Liu, Fang Wang, Hu Hao, Hongxia School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

GPSR-BB ( Gradient Projection for Sparse Reconstruction) algorithm is a popular CS (compressed sensing) reconstruction *** performs well for questions which have sparse *** approach is originally developed in the context of unconstrained minimization of a smooth nonlinear function F,and it uses the search direction of the Quasi-Newton method, So its shortcomings is the same as the Quasi-Newton method. For some reasons,the Hessen matrix of F can't be computed directly,it leads to a performance loss of the *** on GPSR-BB approach,a new gradient projection methods called CGSR-FR (Conjugate Gradient for Sparse Reconstruction) is proposed in this *** experiments show that CGSR-FR approache perform better than GPSR-BB approache in image compression sampling. ©2009 IEEE.

关键词： Compressed sensing

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Unsupervised SAR image segmentation based on quantum-inspired evolutionary gaussian mixture model

Unsupervised SAR image segmentation based on quantum-inspire...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Liu, Fang Liu, Yingying Hao, Hongxia School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In this paper, an unsupervised SAR image segmentation algorithm (QEAGMM) based on quantum-inspired evolutionary Gaussian Mixture Models (GMM) is proposed. The method first divides the original image into small blocks. Then, the heterogeneous and homogeneous blocks are obtained using FCM clustering. Finally, the parameters of gaussian mixture model are trained by expectation-maximization (EM) algorithm using a part of homogeneous samples. However, the EM algorithm is apt to fall into a local optimum and the result is sensitive to initialization. So we embed the EM algorithm in quantum evolutionary algorithm (QEA) and propose a quantum-inspired-based EM algorithm (QEA-EM) to train the gaussian mixture model. This method not only improves the accuracy of parameters estimation but also performs better than immune clonal selection EM algorithm (ICSEM) on computational complexity. The experimental results show that compared to gaussian mixture model clustering algorithm (GMMC), the proposed method is successfully applied to texture mosaic images and SAR images, and shows overall improvement in performance. ©2009 IEEE.

关键词： Synthetic aperture radar

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Automatic computation of fundamental matrix based on voting 1st

Automatic computation of fundamental matrix based on voting

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1st International Conference on Smart Multimedia, ICSM 2018

作者： Li, Xin Sheng Yuan, Xuedong College of Computer Science Sichuan University ChengDu China Key Laboratory of Fundamental Synthetic Vision Graphics and Image for National Defense Sichuan University ChengDu China

ISBN: (纸本)9783030043742

To reconstruct point geometry from multiple images, a new method to compute the fundamental matrix is proposed in this paper. This method uses a new selection method for fundamental matrix under the RANSAC (Random Sample And Consensus) framework. It makes good use of some low quality fundamental matrices to fuse a better quality fundamental matrix. At first, some fundamental matrices are computed as candidates in a few iterations. Then some of the best candidates are chosen based on voting the epipoles of their fundamental matrices to fuse a better fundamental matrix. The fusion can be simple mean or weighted summation of fundamental matrices from the first step. This selection method leads to better result such as more inliers or less projective errors. Our experiments prove and validate this new method of composed fundamental matrix computation. © 2018, Springer Nature Switzerland AG.

关键词： Matrix algebra

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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A comprehensive survey of oracle character recognition: Challenges, datasets, methodology, and beyond

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Pattern Recognition 2025年

作者： Jing Li Xueke Chi Qiufeng Wang Kaizhu Huang Da-Han Wang Yongge Liu Cheng-Lin Liu College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.

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Stability analysis for the generalized Hopfield neural networks with multi-level activation functions

Stability analysis for the generalized Hopfield neural netwo...

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作者： Liu, Yiguang You, Zhisheng Institute of Image and Graphics Key Laboratory of Fundamental Synthetic Vision Graphics and Image for National Defense Sichuan University Chengdu 610064 China School of Computer Science and Engineering Sichuan University Chengdu 610064 China

Using the continuity property of neuron state variables and Lyapunov functional, this paper religiously gives sufficient conditions ensuring the equilibrium number, local stable state number, global stability and complete stability of the Hopfield neural networks with multi-level activation functions. Compared with the known relevant literatures, the obtained results are more precise. Finally, three examples are employed to illustrate the obtained criteria. © 2008 Elsevier B.V. All rights reserved.

关键词： Hopfield neural networks

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Fast Candidate Region Extraction for SAR Ship Target 37

Fast Candidate Region Extraction for SAR Ship Target

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Zhang, Panpan Luo, Haibo Xu, Zheng He, Miao Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China The Key Lab of Image Understanding and Computer Vision Shenyang110016 China

ISBN: (纸本)9781665465366

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset. © 2022 IEEE.

关键词： Synthetic aperture radar

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An adaptive line search scheme for approximated nuclear norm based matrix regression

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Neurocomputing 2018年 289卷 23-31页

作者： Luo, Lei Tu, Qinghua Yang, Jian Yang, Jingyu Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Face recognition with partial occlusion is common in the real world and has become a hot topic in the pattern recognition research. Recently, nuclear norm based matrix regression model (NMR) has been shown a great potential in dealing with the structural noise. And yet, it uses Alternating Direction Method of Multipliers (ADMM) to achieve the optimal regression coefficients, which needs to bring into an auxiliary variable and only exploits the convexity of NMR. Comparing with ADMM, the gradient based methods are simpler. To take advantage of these methods, this paper considers the Approximated NMR model with Elastic-net regularization (ANMRE). By virtue of the soft-thresholding (shrinkage) operator, singular value shrinkage operator and strong convexity of ANMRE, the dual problem of ANMRE (DANMRE) is derived and a crucial result is obtained: the primal optimal solution of ANMRE can be converted as the matrix function associated with the dual optimal solution. Leveraging the differentiability of DANMRE, an adaptive line search scheme is developed to solve it. This approach makes full use of the advantages of the accelerated gradient technique and adaptive parameters updating strategy. Meanwhile, a convergence rate of O(1/N²) can be guaranteed under mild conditions. Experimental results on five standard databases show the superiority of the proposed algorithm over some existing methods. © 2018 Elsevier B.V.

关键词： Adaptive line search scheme Face recognition Gradient method Lipschitz continuous Matrix regression Nuclear norm

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