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A fast quantum mechanics based contour extraction algorithm

A fast quantum mechanics based contour extraction algorithm

引用

Medical Imaging 2009 - image processing

作者： Lan, Tian Sun, Yangguang Ding, Mingyue Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475107

A fast algorithm was proposed to decrease the computational cost of the contour extraction approach based on quantum mechanics. The contour extraction approach based on quantum mechanics is a novel method proposed recently by us, which will be presented on the same conference by another paper of us titled "a statistical approach to contour extraction based on quantum mechanics". In our approach, contour extraction was modeled as the locus of a moving particle described by quantum mechanics, which is obtained by the most probable locus of the particle simulated in a large number of iterations. In quantum mechanics, the probability that a particle appears at a point is equivalent to the square amplitude of the wave function. Furthermore, the expression of the wave function can be derived from digital images, making the probability of the locus of a particle available. We employed the Markov Chain Monte Carlo (MCMC) method to estimate the square amplitude of the wave function. Finally, our fast quantum mechanics based contour extraction algorithm (referred as our fast algorithm hereafter) was evaluated by a number of different images including synthetic and medical images. It was demonstrated that our fast algorithm can achieve significant improvements in accuracy and robustness compared with the well-known state-of-the-art contour extraction techniques and dramatic reduction of time complexity compared to the statistical approach to contour extraction based on quantum mechanics. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Wave functions

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ICSA based projection pursuit clustering with LDA index and its application in SAR image segmentation

ICSA based projection pursuit clustering with LDA index and ...

引用

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

作者： Gou, Shuiping Feng, Jing Jiao, Licheng He, Xiao Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

The performance of the traditional clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, Projection Pursuit with LDA index based on Immune Clonal Selection Algorithm is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimal projection direction. In the proposed algorithm, k-mean clustering algorithm is used to partition the reduced data and give labels to LDA index, and iterations have been done for several times can converge quickly to obtain a clustering result. The experimental results on UCI data show that the present method has better clustering accuracy compared with traditional linear dimension reduction method for principle component analysis (PCA). And the experiments on SAR image segmentation also have a good performance. ©2009 IEEE.

关键词： Clustering algorithms

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SAR image segmentation based on immune greedy spectral clustering

SAR image segmentation based on immune greedy spectral clust...

引用

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

作者： Gou, S.P. Zhang, J. Jiao, L.C. Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In this paper we propose a novel spectral clustering algorithm called Immune Greedy Spectral Clustering Algorithm, which introduces immune clone selection algorithm instead of greedy selection to choose a subset before greedy spectral embedding without any prior experiment knowledge. As we know, Nyström algorithm is a random selecting method which depends so much on the selecting result so that the clustering result fluctuates obviously. Greedy spectral clustering algorithm acquires a subset called dictionary by greedy selection algorithm so that the result could be more stable and better. However, to choose an appropriate input tolerance, we need prior experiment knowledge about the relationship between tolerance and select number. Moreover, since the criterion used for greedy selection is the distance in feature space between a candidate example and its projection on the subspace spanned by selected examples, we need to compute every example point one by one to get the error of using the selected examples to approximate the candidate example and then decide whether to choose it. So the time expense increases inevitable. Considering all above, we present a new method called Immune Greedy Spectral Clustering Algorithm. The experimental results show that Immune Greedy Spectral Clustering Algorithm need no prior experiment knowledge and could save time compared with greedy spectral clustering while getting a better accuracy rate compared with Nyström algorithm. ©2009 IEEE.

关键词： Cloning

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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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Transfer network learning based remote sensing target recognition

Transfer network learning based remote sensing target recogn...

引用

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

作者： Gou, Shuiping Wang, Yuqin Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

The target recognition accuracy of remote sensing images is not satisfied. The labels of images acquisition and recollecting are difficult and expensive. In order to solve the problem, we introduce transfer learning into Network Boosting algorithm (NB) and propose Transfer Network Learning algorithm (TNL), in which other out-date data are reused to instruct the remote sensing target recognition. TNL is suitable to improve the performance of remote sensing target recognition, in which instances transfer learning is adopted for domain adaptation. The experimental results on the MSTAR SAR data set and remote sensing data set including two-class planes show that the proposed algorithm has better performance and achieves different domains learning. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Learning algorithms

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Unsupervised polarimetric SAR image classification using fisher linear discriminant

Unsupervised polarimetric SAR image classification using fis...

引用

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

作者： Chen, Bo Wang, Shuang Jiao, Licheng Zhang, Shuang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

How to use the POLSAR data to classify and interpret the conditions of the earth is a very important research field of POLSAR. In this paper, we propose an improved algorithm on the basis of studying and analyzing some common algorithms. This technique introduces the fisher criterion in the feature selection and the hierarchical method in the classification of POLSAR image, which can improve Lee's combination method of the unsupervised classification based on polarimetric target decomposition and the maximum likelihood classifier based on the complex Wishart distribution. The effectiveness of this algorithm is demonstrated using a polerimetric SAR image. ©2009 IEEE.

关键词： Synthetic aperture radar

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SAR image despeckling based on wavelet kernel transform and gaussian scale mixture model

SAR image despeckling based on wavelet kernel transform and ...

引用

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

作者： Liu, Fan Jiao, Licheng Yang, Shuyuan Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

A new method about SAR image despeckling is proposed in this paper, this method is achieved by combining wavelet kernel transform (WKT) and Gaussian Scale Mixture model (GSM). WKT is a multiscale transform which is based on machine learning model. By analysis the distribution of the coefficients after WKT, these coefficients are similar to Gaussian distribution, and these noised coefficients are distributed as Gaussian too, but are independence with non-noised coefficients. In this paper, we construct the neighbor model based on the coefficients after WKT, and use the Bayes least mean square to despeckle the spots in SAR images, and the model describes the edge distribution of coefficients. We use the proposed method to process the SAR images, and the results demonstrate that this method can obtain better denoising images than Lee filter, wavelet transform etc. ©2009 IEEE.

关键词： Synthetic aperture radar

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Research of method in human detection based on bandelet transform

Research of method in human detection based on bandelet tran...

引用

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

作者： Han, Hong Wu, Xing Jiao, Licheng Yue, Lichuan Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

Based on the geometric flow of images and the second generation bandelet transform, a new feature extraction method was proposed and used to detect human in images. In this paper, bandelet coefficients and their statistical values were extracted as the feature of human images, combined with linear SVM classifier to classify and detect human in images. In the proposed algorithm, some general statistical values of the bandelet coefficients were computed to enhance the classification capacity. The results of human detection experiments indicate that the proposed method can describe human characteristics and improve the performance of classifier effectively. It is very suitable for human detection in images. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Feature extraction

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A method of threaten ordering based on cloud model theory

A method of threaten ordering based on cloud model theory

引用

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

作者： Yu, Xin Wang, Hua Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

Situation assessment (SA) is a complex decision-making process in modern aerial defense system. Threaten ordering (TO) problem is one of the most difficult steps in SA. There are a lot of uncertain factors in TO, and traditional evaluation methods have some disadvantages on algorithm measures. To overcome these disadvantages, a method of TO based on cloud model theory is proposed. Firstly the factors of air-targets threat evaluation are analyzed in this paper. Then the reasoning mechanism base on cloud model is designed. In this step the cloud model is used to depict the attributes of target, the model parameters are defined, and the reasoning rule library is established by using the method of Multiple Attribute Decision Making (MADM). The data of targets were input to the reasoning mechanism, and the threat value was gained. Finally the simulation experiments are given to verify the validity of the method. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Decision making

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An improved watershed-based SAR image segmentation algorithm

An improved watershed-based SAR image segmentation algorithm

引用

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

作者： Wang, Shuang Zhang, Xiaojing Jiao, Licheng Zhang, Xiangrong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

The watershed transform is a well-established tool for image segmentation. However, watershed segmentation is often not effective for Synthetic Aperture Radar (SAR) images which are generally corrupted by coherent speckle noise. This paper presents a novel approach for SAR image segmentation using watershed segmentation algorithm combined with Otsu. The aim of this study is to improve the generalization of watershed techniques and to construct a well segmentation of SAR images. Typically, Otsu is used to produce inner mark and external mark. Then we use the mark to modify the gradient image of the SAR image. Additionally, rather than flooding the gradient image, we use the gradient image modified by the mark as input to the watershed algorithm. Experimental results demonstrate the superior performance of the improved watershed-based SAR segmentation. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image segmentation

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