The tank is one of important military targets. Tanks detection is the study focus of the synthetic aperture radar(SAR) image processing currently. But there may be many false alarms existed in the detection result wit...
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The tank is one of important military targets. Tanks detection is the study focus of the synthetic aperture radar(SAR) image processing currently. But there may be many false alarms existed in the detection result with most of the traditional tank detection methods affected by the SAR speckle. A new method of tank detection for SAR images based on the features of SAR images is put forward by this paper. It uses Gauss low-pass filtering to smooth the original image and the geometric active contour model based on prediction theory metric to realize automatic segmentation. It detects candidate targets by removing little connected regions. Finally, it further removes the false alarm targets based on the grey characteristic of the shadow regions. Experimental results indicate that the method can effectively and rightly detect tanks of SAR images. Moreover, it is insensitive to the initial contour.
A modified method of conventional D-S evidence combination rule was presented. The consensus index of evidence based on distance measures of evidence was introduced into modifying the BPAs of hypotheses with the evide...
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A modified method of conventional D-S evidence combination rule was presented. The consensus index of evidence based on distance measures of evidence was introduced into modifying the BPAs of hypotheses with the evidence sufficiency and evidence importance. By using the D-S combination and new decision rules, the different sets of BPAs for each hypothesis were calculated. A numerical example of the fault diagnosis is used to demonstrate the validity of the method by comparison with other methods.
This paper presents a robust adaptive sliding mode control strategy of MEMS triaixal gyroscope using radial basis function (RBF) neural network. A key property of this scheme is that the prior knowledge of the upp...
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This paper presents a robust adaptive sliding mode control strategy of MEMS triaixal gyroscope using radial basis function (RBF) neural network. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. Adaptive RBF neural network that could learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control scheme in the same Lyapunov framework. The proposed adaptive sliding mode controller can update the estimates of all stiffness errors, damping terms and angular velocities in real time and guarantee the stability of the closed loop system. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.
In this paper, according to the development of the fractional differentiation and its applications in the modern signal processing, we improve the numerical calculation of fractional differentiation by piecewise quadr...
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How to make use of limited memory space and processing speeds of computer for rapid and accurate data mining has become an important research topic on the stream data cluster analysis. A stream data clustering algorit...
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How to make use of limited memory space and processing speeds of computer for rapid and accurate data mining has become an important research topic on the stream data cluster analysis. A stream data clustering algorithm based on the minimum spanning tree (MSTSC) is described. MSTSC is divided into online processing and offline clustering. Stream data are analyzed online by using two groups of processing unit respectively. In offline process clusters is taken as representative objects, and the minimum spanning tree algorithm is used in offline clustering. MSTSC can improve the clustering quality on non-spherical clusters. Some experiments are carried out in both real data sets and synthetic data sets. Results show that MSTSC algorithm not only can deal with non-spherical clusters effectively, but also has better efficiency and clustering quality. In addition, MSTSC is insensitive to order of input data, and has a good effect for skewed class distributions.
For services that have similar functionalities, if they are published by different cloud platforms, it is a challenge to evaluate them, for satisfying different end users' personal preferences. In view of this cha...
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For services that have similar functionalities, if they are published by different cloud platforms, it is a challenge to evaluate them, for satisfying different end users' personal preferences. In view of this challenge, a method for web service ranking, named WSRank, is investigated in cloud environment in this paper. It aims at ranking different Web services published by different cloud platforms, taking advantage of Page Rank principle. At last, a case study and experiment are presented to demonstrate the feasibility of the method.
In traditional fuzzy support vector machine(FSVM), membership function is established in global scope will reduce the membership of support vectors, and the FSVM based dismissing margin increases the training speed, b...
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Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job ...
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ISBN:
(纸本)9781457702099
Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job in solving these problems but still has some flaws. We propose three strategies to try to improve this algorithm: (1) different kernel functions are used to characterize the color and texture visual similarities; (2) a new method is proposed to caculate the confident scores of the contention samples; (3) a bunch of the most irrelevant images with the highest confident score are added into the labeled images to extend the size of labeled data while choosing a bunch of images for user labeling. Experimental results verify the superiority of our method over Co-SVM.
Small sample space target recognition is a difficult problem in applications because the limited training samples cannot lead to satisfactory recognition accuracy. Combined with novel compression perception theory, we...
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Small sample space target recognition is a difficult problem in applications because the limited training samples cannot lead to satisfactory recognition accuracy. Combined with novel compression perception theory, we propose a new space target recognition method based on compressive sensing. This method avoids the sophisticated image preprocessing and feature extraction process. Firstly, a sparse representation dictionary is constructed according to the training samples. Secondly, the linear measurements of test samples are obtained by measurement matrix. Finally, the classification and recognition are done through solving an optimization problem. Simulation experiment results show that the proposed method has good recognition performance and stability.
In addressing side information based face recognition scenario, a new Margin Emphasized Metric Learning (MEML) method is proposed. As an improvement of previous metric learning, MEML defines a new objective function f...
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In addressing side information based face recognition scenario, a new Margin Emphasized Metric Learning (MEML) method is proposed. As an improvement of previous metric learning, MEML defines a new objective function for optimization, which adds more weights to sample pairs on the boundary thus hard to classify. To further improve face verification performance, MEML is applied to Gabor feature in a block dividing and combining mode. Experiments on LFW image-restricted setting illustrate very impressive performance compared with traditional methods. By combining multiple MEML classifiers on several features, performance comparable to the best known results on LFW is achieved.
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