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检索条件"机构=Key Laboratory of Intelligent Perception and Image Understanding"

共 931 条记录，以下是121-130 订阅

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Approximation of functions with spatial inhomogeneity based on "true" ortho-ridgelet neural network

Approximation of functions with spatial inhomogeneity based ...

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作者： Yang, Shuyuan Wang, Min Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding Xidian University Ministry of Education of China Xi'an 710071 China

To approximate the multivariate functions with spatial inhomogeneity, in this paper we proposed an ortho-ridgelet neural network (ORNN) model. By taking orthonormal ridgelet, which is a "true" ridgelet function different with the "classic" ridgelet, as the activation function of the hidden neurons, the network is characterized of more efficient representation of a set of functions with linear and curvilinear singularities. Although the ortho-ridgelet is initially more complicated to be understood than ridgelet, it can be easily applied to function approximation theory in a more effective manner. Moreover, we extended the ORNN model to the time-varied situation, and the time-varied ortho-ridgelet neural network (TV-ORRN) is established for time-varied function approximation, with an incremental learning of the ridgelets output layer. The theoretical analysis is presented and some experiments are taken, and the experimental results prove its efficiency in approximation of multivariate functions with linear and curvilinear singularities, in both the stationary and time-varied environment. © 2010 Elsevier B.V. All rights reserved.

关键词： Neural network models

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Remote sensing image registration with spatial restraint based on moment invariants and fast generalized fuzzy clustering

Remote sensing image registration with spatial restraint bas...

引用

Conference on Technologies and Applications of Artificial Intelligence, TAAI 2015

作者： Yue, Wu Maoguo, Gong Jia, Ji Ma, Wenping Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an710071 China

ISBN: (纸本)9781467396066

image registration is a key component in remote sensing image processing. In this paper, we present a remote sensing image registration method by incorporating spatial restraint based on moment invariants and fast generalized fuzzy clustering. Seven moment invariants are extracted as features of objects obtained by the fast generalized fuzzy c-means (FGFCM) algorithm. The objects are matched through these features. Then, we detect the keypoints in corresponding matching regions. Through the spatial restraint, the outliers are removed and the correct matches are increased. The proposed algorithm is evaluated on multi-spectral images, multi-temporal images, and multi-sensor images. Extensive experimental studies prove that the proposed algorithm is promising. © 2015 IEEE.

关键词： Remote sensing

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A parallel artificial immune model for optimization

A parallel artificial immune model for optimization

引用

2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Qi, Yutao Liu, Fang Jiao, Licheng School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Xidian University Xi'an China Inst. of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

ISBN: (纸本)9780769539317

This paper presents a parallel artificial immune model termed as tower master-slave model (TMSM) for solving optimising problems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than individual migration as has been done in parallel genetic algorithms. It is a good balance between population diversity and the convergent speed. Experimental results on the numerical optimization and TSP problems show that PIMCSA achieves good performance in terms of both solution quality and computation time. © 2009 IEEE.

关键词： Genetic algorithms

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SAR image despeckling method based on dual-tree complex wavelet

SAR image despeckling method based on dual-tree complex wave...

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6th International Conference on Radar, RADAR 2011

作者： Wang, Shuang Zhou, Jiao Li, Jun Feng, Hongxiao Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xidian University Xi'an 710071 China

We propose a despeckling method based on dual-tree complex wavelet transform (DTCWT) for SAR image. The Gaussian model is used to model the non-logarithmic additive noise of the local isotropic region. Shrinkage funct... 详细信息

ISBN: (纸本)9781424484416

关键词： Additive noise

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Local manifold spectral clustering with FCM data condensation

Local manifold spectral clustering with FCM data condensatio...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Liu, Hanqiang Jiao, Licheng Zhao, Feng Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9780819478078

In this paper, a novel local manifold spectral clustering with fuzzy c-means (FCM) data condensation is presented. Firstly, a multilayer FCM data condensation method is performed on the original data to contain a condensation subset. Secondly, the spectral clustering algorithm based on the local manifold distance measure is used to realize the classification of the condensation subset. Finally, the nearest neighbor method is adopted to obtain the clustering result of the original data. Compared with the standard spectral clustering algorithm, the novel method is more robust and has the advantages of effectively dealing with the large scale data. In our experiments, we first analyze the performances of multilayer FCM data condensation and local manifold distance measure, then apply our method to solve image segmentation and the large Brodatz texture images classification. The experimental results show that the method is effective and extensible, and especially the runtime of this method is acceptable. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Condensation

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Spectral clustering with eigenvector selection based on ensemble ranking

Spectral clustering with eigenvector selection based on ense...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Zhao, Feng Jiao, Licheng Liu, Hanqiang Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9780819478078

Ng-Jordan-Weiss (NJW) method is one of the most widely used spectral clustering algorithms. For a clustering problem with K clusters, this method clusters data using the largest K eigenvectors of the normalized affinity matrix derived from the data set. However, the top K eigenvectors are not always the most important eigenvectors for clustering. In this paper, we propose an eigenvector selection method based on an ensemble of multiple eigenvector rankings (ESEER) for spectral clustering. In ESEER method, first multiple rankings of eigenvectors are obtained by using the entropy metric, which is used to measure the importance of each eigenvector, next the multiple eigenvector rankings are aggregated into a single consensus one, then the first K eigenvectors in the consensus ranking list are adopted as the selected eigenvectors. We have performed experiments on artificial data sets, standard data sets of UCI repository and handwritten digits from MNIST database. The experimental results show that ESEER method is more effective than NJW method in some cases. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Eigenvalues and eigenfunctions

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Quantum-inspired immune memory algorithm for self-structuring antenna optimization

Quantum-inspired immune memory algorithm for self-structurin...

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International Conference on Computer Science and Software Engineering, CSSE 2008

作者： Qiuyi, Wu Xiaoying, Pan Licheng, Jiao Yifei, Sun Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xidian University Xi'an 710071 China

ISBN: (纸本)9780769533360

Based on the concept and principle of quantum computing and immune system, a novel optimization technique, called a quantum-inspired immune memory algorithm is proposed to deal with the problem of the optimization of the switches of the self-structuring antenna. In the algorithm, the proposed memory strategy realizes the information transfer between the courses of evolution. Theoretical analysis proves that quantum-inspired immune memory algorithm converges to the global optimum. The feasibility, efficiency and effectiveness of the proposed algorithm for optimization of self-structuring antenna, whose performance is analyzed by the fast moment method (Mom) are examined. The results show that quantum-inspired immune memory algorithm performs much better than the genetic algorithms in terms of the quality of solution and convergence speed. In addition, parameter analysis demonstrates our algorithm has stable performance and is insensitive to the change of parameters. © 2008 IEEE.

关键词： Antennas

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A memetic algorithm based on decomposition and extended search for multi-objective capacitated arc routing problem 11th

A memetic algorithm based on decomposition and extended sear...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Shang, Ronghua Yuan, Yijing Du, Bingqi Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Xidian University Xi’anShanxi710071 China

ISBN: (纸本)9783319687582

The capacitated arc routing problem is a classical NP-hard problem to solve in the field of combinatorial optimization. In recent years, due to its extensive use in our daily life, its importance has gradually emerged. Multi-objective capacitated arc routing problem (MO-CARP) is more close to real life, so it arouses widespread concern. The Multi-objective evolution algorithm based on decomposition provides a suitable frame for solving MO-CARP. In this paper, a memetic algorithm based on decomposition and extended search (ED-MAENS) is proposed to deal with MO-CARP. Firstly, decompose the MO-CARP into many single-objective sub-problems using weight vectors. Then assign represent solution for each single-objective problem. To make sure that each single-objective problems can get a reasonable represent solution, the rank conception is proposed. After that, MAENS algorithm is adopted to solve each single-objective problem using the information of its neighborhood. Finally, we proposed an extended search operator to enlarge the searching space to improve the solution quality. The new proposed algorithm is evaluated on medium and large scale instance set and experimental results demonstrate the proposed method can obtain the better non-dominated solution than compared algorithms especially on large-scale instance. © Springer International Publishing AG 2017.

关键词： Combinatorial optimization

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A change detection algorithm based on object feature for SAR image

A change detection algorithm based on object feature for SAR...

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

作者： Zhang, Xiaohua Li, Hongfeng Jiao, Licheng Institution of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In the detection of change objects of SAR images, regard to the images containing high intensity noise, traditional methods will produce a considerable false alarm. This paper presents a new method of change detection based on directional information. Direction measure is proposed to suppress noises and brighten object contours. Then region blocks which contain changing objects are extracted according to the difference map of direction measure. Finally, changed objects can be obtained using the OTSU threshold method for each region block. The experimental results on SAR images demonstrate the efficiency and accuracy of the proposed method. ©2009 IEEE.

关键词： Synthetic aperture radar

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Classification based nonlocal means despeckling for SAR image

Classification based nonlocal means despeckling for SAR imag...

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Zhong, Hua Xu, Jingjing Jiao, Licheng Institution of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

The nonlocal (NL) means filter as a recent denoising approach has demonstrated its empirical merit for additive Gaussian noise. In this paper, a new nonlocal means despeckling method for synthetic aperture radar (SAR) image is proposed, which is adapted to the multiplicative model of speckle noise. The proposed method still uses Euclidean distance based similarity measure but adopting a strategy of pixel classification, which can effectively reduce the influence of the multiplicative speckle model and improve the effectiveness in searching of similar patches, thus contributes to the final results. By this strategy, image pixels are first classified into different classes such as point, line, edge, etc., and then different smooth parameters of nonlocal means filter are used according to the class information. In addition, a searching method for rotation-invariant similar patches is designed through the use of directional information. We validate the proposed method on real synthetic aperture radar (SAR) images and confirm the excellent despeckling performance through comparisons with other classical despeckling methods, such the Enhanced Lee filter, Enhanced Gamma MAP filter, wavelet thresholding, as well as original NL mean filter. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image classification

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