Improved data association methods for multi-target tracking are discussed in this paper. We introduce an exquisite gating technique based on the well-known probabilistic data association (PDA) filter, which calculates...
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The paper presents a new fast interpolation method based on edge *** method extracts image edge by improved canny operator which has two specified thresholds *** for different areas,it chooses different *** can keep e...
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The paper presents a new fast interpolation method based on edge *** method extracts image edge by improved canny operator which has two specified thresholds *** for different areas,it chooses different *** can keep edge details while doing quick *** experimental results show that the effect of this method can hold the edge details very well,and it is obviously superior to the traditional *** method provides a better initial image for the super resolution reconstruction.
Safety supervisory systems continue to increase in degree of automation and complexity as operators are decreasing. As a result, each operator must be able to comprehend and respond to an ever increasing amount of ava...
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ISBN:
(纸本)9781467315074
Safety supervisory systems continue to increase in degree of automation and complexity as operators are decreasing. As a result, each operator must be able to comprehend and respond to an ever increasing amount of available risky status and alert information. They generally have no difficulty in performing their tasks physically but they are stressed by the task of understanding what is going on in the situation. So in the last two decades, situation awareness has been recognized as a critical foundation for successful decision making across a broad range of complex and dynamic systems. This paper develops a fuzzy dual expert system based approach to enhance situation awareness. The proposed approach has ability to support the operators' understanding and assessing the situations, and to deal with uncertainties, applying fuzzy risk assessment concepts.
With the success of internet, recently more and more companies start to run web-based business. While running e-business sites, many companies have encountered unexpected degeneration of their web server applications ...
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Diagnosis for wireless sensor networks is difficult, due to the limited resources and the ad hoc manner of networks. The existing approaches mainly focus on collecting diagnosis metrics, which bring heavy communicatio...
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Diagnosis for wireless sensor networks is difficult, due to the limited resources and the ad hoc manner of networks. The existing approaches mainly focus on collecting diagnosis metrics, which bring heavy communication overhead to the network. We present a new model called DSD for network diagnosis which deduce the root causes for failures using the sensing data traces. We discover that the characteristics of the sensing data reflect the network status in some way, according to considerable experiments in the GreenOrbs project. We mine the relationships between the sensing data and the failures in the sensor networks, and record them in a failure knowledge library. Through this diagnosis mechanism, we deduce the root cause of the failures without adding any additional network burden. Moreover, the failure knowledge library can be used to improve the efficiency of diagnosis. We analyze the three months sensing data from the GreenOrbs project, and experimental results show that the proposed scheme can improve the diagnosis performance with low energy cost.
In recent years,the threshold for removing noise based on wavelet transform has been very widely used because of its effectiveness and ***,there has been threshold based on a variety of frequency-domain *** the proces...
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In recent years,the threshold for removing noise based on wavelet transform has been very widely used because of its effectiveness and ***,there has been threshold based on a variety of frequency-domain *** the process of denoising,due to the differentiation of transform coefficients generated by noise and edge information,a good threshold for denoising can make a significant impact on the image *** currently existing threshold,spatially adaptive threshold based on Context-Modeling is proposed because of having considered neighboring coefficients so that it can adjust to coefficient *** this paper the improved spatially adaptive threshold method is applied to the nonsubsampled contourlet *** results show that the method yields superior image quality and higher PSNR.
To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learn...
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To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learning into the Adaboost learning framework and at the same time leverages the advantages of Co-training algorithm for performance enhancement. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of the convergence rate, the accuracy, and the steady performance as compared to the original AdaBoost algorithm, without relying on redundant and sufficient feature sets. As a algorithm application in softwareengineering, the Embedded Co-AdaBoost has been applied to the classification of software document relations to improve the quality of the architecture design documents and the reusability of design knowledge.
In some learning approach problems, the learning algorithm receives training and test samples drawn according to the same distribution. However, this assumption is not available in practice. In face super-resolution, ...
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In some learning approach problems, the learning algorithm receives training and test samples drawn according to the same distribution. However, this assumption is not available in practice. In face super-resolution, when the training sample available is biased, it may affect quality of the construction face. In this paper, we proposed a novel method to correct the bias of sample selection in training dataset. First, Active Shape Model is used to get the face shape vectors which contain some information about face contour. Then all faces from Chinese face dataset are classified into certain categories based on Hausdor ff Distance by k-means clustering. We correct the sample bias by selecting the most similar faces due to the face shape similarity between samples and test images. Last, the global face reconstruction method based on eigen face is used to achieve satisfied image quality with selected train dataset. Experiments show that the face super-resolution algorithm based on sample selection bias correction can improve the subjective and objective quality of the input low resolution face images compared to traditional eigenface algorithm.
Lesion segmentation plays an important role in medical image processing and analysis. There exist several successful dynamic programming (DP) based segmentation methods for general images. In those methods, the gradie...
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This paper aims to propose a practical set of features for representing the visual speech of Chinese phonemes. The state and hence visibility of teeth and tongue play important roles in pronunciation, but discriminati...
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