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检索条件"主题词=detection Algorithms"

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A skin detection algorithm based on discrete Cosine transform and generalized Gaussian density

A skin detection algorithm based on discrete Cosine transfor...

引用

IEEE International Conference on Image Processing

作者： S. Ghouzali S. Hemami M. Rziza D. Aboutajdine E. M. Mouaddib GSCM-LRIT Mohammed-V University Rabat Morocco School of Electrical and Computer Engineering Cornell University Ithaca NY USA MIS Universite de Picardie Jules Verne Amiens France

In this paper, we propose a highly efficient algorithm to model the human skin color. The underlying algorithm involves generating a discrete Cosine transform (DCT) at each pixel location, using the surrounding points. These DCT coefficients are assumed to follow a generalized Gaussian distribution (GGD). Next, the model parameters are estimated using the maximum-likelihood (ML) criterion applied to a set of training skin samples. Finally, each pixel is classified as skin or the opposite if its likelihood ratio is above some threshold. The experimental results show that our model avoids excessive false detection while still retaining a high degree of correct detection.

关键词： Skin detection algorithms Discrete cosine transforms Color Maximum likelihood estimation Face detection Shape Image segmentation Pixel Robustness

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Using Outlier detection to Reduce False Positives in Intrusion detection

Using Outlier Detection to Reduce False Positives in Intrusi...

引用

IFIP International Conference on Network and Parallel Computing (NPC)

作者： Fu Xiao Xie Li State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Intrusion detection systems (IDSs) can easily create thousands of alerts per day, up to 99% of which are false positives (i.e. alerts that are triggered incorrectly by benign events). This makes it extremely difficult for managers to analyze and react to attacks. This paper presents a novel method for handling IDS alerts more efficiently. It introduces outlier detection technique into this field, and designs a special outlier detection algorithm for identifying true alerts and reducing false positives. This algorithm uses frequent attribute values mined from historical alerts as the features of false positives, and then filters false alerts by the score calculated based on these features. We also proposed a two-phrase framework, which not only can filter newcome alerts in real time, but also can learn from these alerts and automatically adjust the filtering mechanism to new situations. Moreover our method needs no domain knowledge and little human assistance, so it is more practical than current ways. We have built a prototype implementation of our method. And the experiments on DARPA 2000 and real-world data have proved that this model has high performance.

关键词： Intrusion detection Filters detection algorithms Algorithm design and analysis Filtering Machine learning algorithms Delay Parallel processing Event detection Laboratories

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A Musical Onset detection System with Scalable Complexity

A Musical Onset Detection System with Scalable Complexity

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Wan-Chi Lee Yu Shiu C.-C. Jay Kuo Ming Hsieh Department of Electrical Engineering University of Southern California USA

An onset detection system based on linear prediction with scalable complexity is proposed in this work. One unique feature of the proposed onset detection algorithm is that it can offer a trade-off between complexity and detection accuracy by adjusting its parameters. Consequently, it can be used in consumer electronics such as karaoke performance evaluation and automatic visual effect generation in portable media players.

关键词： detection algorithms Portable media players Consumer electronics Visual effects Multiple signal classification Nonlinear filters Smoothing methods Personal communication networks Signal generators Computer errors

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Novel detection Algorithm of IDMA System under Channel Estimation Error

Novel Detection Algorithm of IDMA System under Channel Estim...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Chulhee Jang Hyunwoo Choi Jae Hong Lee School of Electrical Engineering and INMC Seoul National University Seoul South Korea

In this paper, we propose a novel detection algorithm for the elementary signal estimator of an IDMA system considering channel estimation error. To develop the algorithm, we derive new probability density function of decision variable reflecting channel estimation error and modify the conventional algorithm based on it. Through computer simulations, it is shown that the proposed algorithm achieves lower bit error rate than the conventional one. This performance enhancement is provided with negligible increase of its computational complexity.

关键词： Channel estimation Complexity theory Probability density function Multiaccess communication detection algorithms Multiuser detection Interference

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An Intrusion detection Research Based on Spectral Clustering

An Intrusion Detection Research Based on Spectral Clustering

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Min Luo Xiaohong Li Shouhe Xie

A spectral clustering intrusion detection approach is presented in this paper. The basic idea of the approach is to compute the similarities between the training data points, then to construct the affinity matrix, and to get the clusters according the main eigenvector of this affinity matrix. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio. The benefits of the approach lie in that it is accurate in clustering and it needn 't labeled training data sets. Using the data sets of KDD99, the experiment result shows that this approach can detect intrusions efficiently in the real network connections.

关键词： Intrusion detection Clustering algorithms Training data Military computing detection algorithms Algorithm design and analysis Data mining Partitioning algorithms Clustering methods Data security

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The Discovery of Attribute Feature Cluster for Any Clustering Result Based on Outlier detection Technique

The Discovery of Attribute Feature Cluster for Any Clusterin...

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International Conference on Internet Computing in Science and Engineering (ICICSE)

作者： Gang Liu Jiming Pang Xiufeng Piao Shaobin Huang College of Computer Science and Technology Harbin Engineering of Technology Harbin Heilongjiang China College of Computer Science and Technology Jilin University Changchun Jilin China

The comprehension of the clustering result is a problem that hasn't yet resolve, which having important meaning to the usage of the cluster result and the evaluation of the cluster effect. We put forward the method discovering attribute feature cluster for any clustering result based on outlier detection technique, and put forward an outlier detection algorithm based on even distribution pattern. Through carrying on outlier analysis to all data cluster attribute descriptions, we discovered the feature attribute of each data cluster, and then carried out the comprehension to the clustering result. The remarkable point lies in the method doesn't only aim at a particular clustering algorithm, but also the analysis of any the clustering algorithm result. Experiment to the UCI data set indicated, the method submitted in this paper obtained better result.

关键词： detection algorithms Clustering algorithms Algorithm design and analysis Educational institutions Computer science Data analysis Data visualization Multidimensional systems Internet Displays

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Unsupervised automatic change detection in images under time-varying illumination

Unsupervised automatic change detection in images under time...

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IEEE Region 10 International Conference TENCON

作者： N. D. Atkekar Sayeed Ahmed Khan M. A. Joshi Pune India Department of Electronics and Telecommunication College of Engineering Department of Electronics and Telecommunication College of Engineering Pune India

It is not surprising that the process of change detection is fundamental to many machine vision applications. Most change detection algorithms assume that the illumination on a scene will remain constant. Unfortunately, this assumption is not necessarily valid outside a well-controlled laboratory setting. The accuracy of existing algorithms decreases significantly when faced up with image sequences in which the illumination is allowed to vary. In this paper an unsupervised change detection algorithm has been proposed, it performs the change detection without any additional information besides the raw images considered. It performs well under time-varying illumination conditions, where other algorithms fail to perform.

关键词： Lighting Layout detection algorithms Change detection algorithms Machine vision Educational institutions Image motion analysis Algorithm design and analysis Pixel Laboratories

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A detection Algorithm of Audio Spread Spectrum Data Hiding

A Detection Algorithm of Audio Spread Spectrum Data Hiding

引用

International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： S. Gao R. M. Hu W. Zeng H. J. Ai C. R. Li National Engineering Research Center for Multimedia Software Wuhan University of China Wuhan China

In this paper, a method of passive steganalysis is proposed. We focus on detecting the existing of data hidden in audio files with spread spectrum (SS) data hiding. SS data hiding is considered as a process of adding noise. The technology of classifier and feature vector extraction are used to achieve the detection. First, we divide an audio signal into several frames. The wavelet coefficients before and after wavelet de-noise in each frame are calculated. Then, we pick some stat, of their difference as the feature vectors of the audio signal. Finally, according to the feature vectors of the audio signal, classifier will decide whether the audio signal have been processed by SS or not. In our experiment, support vector machines (SVM) play role of classifier, 600 audio files are used to be our experiment samples. After the feature vectors of all the samples are calculated, those feature vectors of samples are divided into two parts. One is testing part and the other is training part. The result of experiment shows that if the strength of data hiding is higher than 0.005, the rate of correct detection of training part is higher than 86.5% and the rate of correct detection of testing part is higher than 82.5%.

关键词： detection algorithms Spread spectrum communication Data encapsulation Signal processing Support vector machines Support vector machine classification Testing Feature extraction Data mining Wavelet coefficients

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Effects of Channel Estimation Errors on V-BLAST detection

Effects of Channel Estimation Errors on V-BLAST Detection

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IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

作者： Wei Peng Fumiyuki Adachi Shaodan Ma Jiangzhou Wang Tung-Sang Ng Department of Electrical and Communication Engineering University of Tohoku Japan Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong China Department of Electronics University of Kent UK

This paper investigates the effects of channel estimation errors on zero-forcing (ZF) vertical Bell laboratories layered space time (V-BLAST) detection. An analytical method is presented to derive the symbol error probability (SEP) of the signals detected at each stage. The effects of imperfect channel estimation on the SEP performance of V-BLAST detection are then studied. It is shown that ZF-VBLAST detection is very sensitive to the channel estimation errors under high signal to noise ratio (SNR). It is also shown that when optimal ordering is adopted, the effects of channel estimation errors are more significant on the latter detection stages.

关键词： Channel estimation MIMO Receiving antennas Signal analysis Error probability Performance analysis AWGN Signal detection Signal to noise ratio detection algorithms

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Evaluation of an Online Parallel Anomaly detection System

Evaluation of an Online Parallel Anomaly Detection System

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IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

作者： Shashank Shanbhag Tilman Wolf Department of Electrical and Computer Engineering University of Massachusetts Amherst MA USA

The rapid and accurate detection of anomalies in network traffic has always been a challenging task, and is absolutely critical to the efficient operation of the network. The availability of numerous different detection algorithms makes it difficult to choose a suitable configuration. An algorithm may have a high detection rate for high rate attacks, but might behave unfavorably when faced with attacks with gradually increasing rates. This paper proposes an online parallel anomaly detection system that implements multiple anomaly detection algorithms in parallel to detect anomalies in real-time. The main idea is to aggregate the detection data from multiple algorithms to come up with a single anomaly metric. We evaluate this system with realistic attacks on the DETER testbed. Our results show improved true positive and false negative rates for both high intensity and slow-rise ramped floods. Furthermore, the system is able to detect attacks separated by as little as 15 seconds with a high true positive rate.

关键词： Telecommunication traffic detection algorithms System testing Face detection Real time systems Aggregates Floods Statistical analysis Monitoring Prototypes

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