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International Symposium on Intelligent multimedia, Video and Speech processing

作者： Xiaomei Yu D.B. Hoang D.D. Feng Basser Department of Computer Science University of Sydney NSW Australia Center for Multimedia Signal Processing Department of Electronic & Information Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)9628576623

In this paper, we proposed a novel weight-based bandwidth allocation algorithm (WBA) for transporting compressed video traffic using feedback. Extensive simulation using a modified NIST simulator is conducted to evaluate its performance under a general weight-based share policy. Our results demonstrate that the WBA algorithm is capable of allocating bandwidth fairly for the minimum cell rate (MCR) plus weighted allocation fairness criteria among competitive compressed video sources, and is capable of reallocating smoothly when there are renegotiations of the minimum guaranteed cell rate and weight by some connections. We also show that WBA algorithm is flexible for the reallocation when a new connection is admitted. Furthermore, when unutilized network bandwidth becomes available, our algorithm is capable of exploiting it to dynamically allocate unused bandwidth.

关键词： Video compression Bandwidth signal processing algorithms Video sharing Channel allocation Erbium Switches Feedback Asynchronous transfer mode Protocols

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A fuzzy metric for image quality assessment

A fuzzy metric for image quality assessment

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Junli Li Gang Chen Zheru Chi Institute of Sciences University of Zhejiang Hangzhou China Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Hong Kong China

ISBN: (纸本)078037293X

Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L/sup 2/-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with human visual perception in many cases. In this paper, a fuzzy image metric (FIM) is defined based on M. Sugeno's (1974) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgement procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using PSNR.

关键词： Image quality Humans Distortion measurement Mathematical model Visual system PSNR Visual perception Additive noise Frequency measurement Noise measurement

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Human face recognition using a spatially weighted Hausdorff distance

Human face recognition using a spatially weighted Hausdorff ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Baofeng Guo Kin-Man Lam Wan-Chi Siu Shuyuan Yang Chinese Academy of Sciences Institute of Acoustics UK Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Hong Kong China

ISBN: (纸本)0780366859

The edge map of a facial image contains abundant information about its shape and structure, which is useful for face recognition. To compare edge images, Hausdroff distance is an efficient measure that can determine the degree of their resemblance, and does not require a knowledge of correspondence among those points in the two edge maps. In this paper, a new modified Hausdorff distance measure is proposed, which has a better noise immunity capability and better discriminant power. As the different facial regions have different relative importance for face recognition, the modified Hausdorff distance is weighted according to a weighted function derived from the spatial. Information of the human face; hence crucial regions are emphasized for face identification. Experimental results show that the distance measure can achieve recognition rates of 82%, 93%, and 97% for the first, the first three, and the first five likely matched faces, respectively.

关键词： Humans Face recognition Face detection Power measurement Surveillance Fingerprint recognition Shape measurement Robustness Image databases Spatial databases

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PCR-based fair intelligent bandwidth allocation for rate adaptive video traffic

PCR-based fair intelligent bandwidth allocation for rate ada...

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International Symposium on Intelligent multimedia, Video and Speech processing

作者： X. Yu D.B. Hoang D.D. Feng Basser Department of Computer Science University of Sydney NSW Australia Center for Multimedia Signal Processing Department of Electronic & Information Engineering Hong Kong Polytechnic University Hung Hom Hong Kong China

ISBN: (纸本)9628576623

In this paper, we propose a network bandwidth sharing algorithm, Peak Cell Rate (PCR)-based Fair Intelligent Bandwidth Allocation (PFIBA) for transporting rate-adaptive video traffic using feedback, and report on its performance under a general PCR-based share policies. Through extensive simulations, we obtained following results. The PFIBA algorithm is capable of allocating bandwidth fairly for the minimum cell rate (MCR) plus PCR-proportional fairness criteria among competitive rate-adaptive video sources, is capable to reallocate smoothly when there are renegotiations of the minimum guaranteed cell rate or PCR by some connections, is able to reallocate smoothly when a new connection is admitted, and is able to reallocate smoothly when a connection is throttled somewhere earlier along the connection path. Furthermore, we show the algorithm prevents congestion, especially during the initial periods when buffer queues can build up significantly.

关键词： Channel allocation Bandwidth Video sharing Video compression Traffic control signal processing algorithms Switches Feedback control Communication system traffic control Convergence

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A novel QoS feedback control for supporting compressed video

A novel QoS feedback control for supporting compressed video

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： Xiaomei Yu D.B. Hoang Dagan Feng Basser Department of Computer Science University of Sydney NSW Australia Nortel Networks Limited Santa Clara CA USA Center for Multimedia Signal Processing Department of Electronic & Information Engineering Hong Kong Polytechnic University Hong Kong China

This paper proposes a novel application layer quality of service (QoS) feedback control scheme for supporting compressed video. In this proposal, a QoS packet is sent by the source after each video frame to transfer QoS information. The source employs a simple additive-increase and explicit-decrease bandwidth algorithm to adjust its rate based on the QoS feedback parameters sent from the destination. The simulation results show that the proposed scheme is able to control the jitter, delay and loss very efficiently.

关键词： Feedback control Video compression Delay Jitter Quality of service signal processing algorithms Programmable control Adaptive control Streaming media Protocols

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A QoS control protocol for rate-adaptive video traffic

A QoS control protocol for rate-adaptive video traffic

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IEEE International Conference on Networks

作者： Xiaomei Yu D.B. Hoang Dagan Feng Basser Department of Computer Science University of Sydney NSW Australia Nortel Networks Limited Santa Clara CA USA Center for Multimedia Signal Processing Department of E:lectronic & Information Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0769511864

We present an application layer quality of service (QoS) control protocol (QCP), which employs an additive-increase bandwidth feedback algorithm. The protocol enhances the communication between end nodes and "the network" by a QoS packet sent from the source for per n video frames. The destination calculates the delay jitter, throughput for per QoS interval, and sends a backward QoS packet to the source. Upon the receipt of a QoS packet, the source use the delay and the bandwidth contained in the QoS packet to adjusts its sending rate. The protocol is well suited for real-time rate-adaptive video streams. The primary goal is to be easy to implement and provide an efficient application layer QoS control. Through extensive simulations, we conclude that the protocol could be able to control the jitter, delay and loss very efficiently.

关键词： Protocols Quality of service Delay Communication system traffic control Bandwidth Jitter Communication system control Feedback Throughput Streaming media

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Cloud detection using probabilistic neural networks

Cloud detection using probabilistic neural networks

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： W.D. Zhang M.X. He M.W. Mak Ocean Remote Sensing Laboratory of Ministry of China Ocean Remote Sensing Institute Ocean University of China China Center of Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China

This paper investigates the application of a particular type of probabilistic neural networks, namely radial basis function (RBF) networks, to detecting cloud in NOAA/AVHRR images. Based on the images collected from the East China Sea, the paper compares the performance of RBF networks with that of traditional multi-layer perceptrons (MLPs). The main results show that RBF networks are able to handle complex atmospheric and oceanographic phenomena while MLPs could not. The internal representation of the RBF networks and MLPs are also detailed in this paper.

关键词： Clouds Neural networks Character generation Gold

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A new two-stage scoring normalization approach to speaker verification

A new two-stage scoring normalization approach to speaker ve...

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International Symposium on Intelligent multimedia, Video and Speech processing

作者： M.W. Mak W.D. Zhang M.X. He Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Hong Kong China Ocean Remote Sensing Laboratory of Ministry of Education Ocean Remote Sensing Institute Ocean University of China China

ISBN: (纸本)9628576623

In speaker verification, the cohort and world models have been separately used for scoring normalization. The authors embed the two models in elliptical basis function networks and propose a two-stage decision procedure for improving verification performance. The procedure begins with normalization of an utterance by a world model. If the difference between the resulting score and a world threshold is sufficiently large, the claimant is accepted or rejected immediately. Otherwise, the score will be normalized by a cohort model, and the resulting score will be compared with a cohort threshold to make a final accept/reject decision. Experimental evaluations based on the YOHO corpus suggest that the two-stage method achieves a lower error rate as compared to the case where only one background model is used.

关键词： Oceans Decision making Robustness Remote sensing signal processing Helium Laboratories Error analysis Speech processing Covariance matrix

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Application of MLP and RBF networks to cloud detection

Application of MLP and RBF networks to cloud detection

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International Symposium on Intelligent multimedia, Video and Speech processing

作者： W.D. Zhang M.X. He M.W. Mak Ocean Remote Sensing Laboratory of Ministry of Education of China Ocean Remote Sensing Institute Ocean University of China China Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Hong Kong China

ISBN: (纸本)9628576623

The paper compares the performances of multilayer perceptrons (MLPs) and radial basis function (RBF) networks in detecting clouds in NOAA/AVHRR images. The main results show that the RBF networks are able to handle complex atmospheric and oceanographic phenomena while conventional rule-based systems and MLPs cannot. In particular, the experimental evaluations show that the RBF networks can converge to global minima while the MLPs can only achieve this occasionally, and that classification errors made by the RBF networks decrease dramatically when the number of basis functions increases. In addition, these errors are almost identical when the number of basis functions reaches a threshold. Only on a few rare occasions does the backpropagation algorithm attain an optimal solution and the classification errors made by the MLPs are comparable to (but still larger than) the ones made by the RBF networks. However, the results show that achieving such optimal solutions is difficult. It is, therefore, concluded that the RBF networks are better than the MLPs for cloud detection.

关键词： Radial basis function networks Clouds Oceans Backpropagation algorithms Remote sensing Knowledge based systems Image converters Multilayer perceptrons Feedforward systems Neural networks

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A semi-parametric hybrid neural model for nonlinear blind signal separation.

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International journal of neural systems 2000年第2期10卷 79-93页

作者： Peng, H. Chi, Z. Siu, W. Center for Multimedia Signal Processing Department of Electronic &amp Information Engineering The Hong Kong Polytechnic University Kowloon Hong Kong

Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases.

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