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检索条件"机构=Multimedia Signal Processing and Pattern Recognition Laboratory"

共 90 条记录，以下是61-70 订阅

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A PERCEPTUALLY ENHANCED BLIND SINGLE-CHANNEL AUDIO SOURCE SEPARATION BY NON-NEGATIVE MATRIX FACTORIZATION

A PERCEPTUALLY ENHANCED BLIND SINGLE-CHANNEL AUDIO SOURCE SE...

引用

European signal processing Conference

作者： S. Kubiz B. Gunsel Multimedia Signal Processing and Pattern Recognition Lab. Istanbul Technical University Dept. of Electronics and Communications Engineering

This paper proposes a 2D Non-negative Matrix Factorization (NMF) based single-channel source separation algorithm that emphasizes perceptually important components of audio. Unlike the existing methods, the proposed scheme performs a psychoacoustic pre-processing on the mixture spectrogram in order to suppress audio components that are not critical to human hearing sensation while amplifying the perceptually important ones. This yields the auditory spectrogram referred as sonogram of the observed audio mixture and the individual sources are then extracted by 2D NMF. Test results reported in terms of signal-to-Distortion-Ratio (SDR), signal-to-Inference-Ratio (SIR) and signal-to-Artifact-Ratio (SAR) show that the proposed perceptually enhanced separation improves the quality of decomposed audio sources by 1.5-6.5 dB with a reduced computational complexity.

关键词： Audio matrix decomposition Sonogram factorization complexity classes Spectrogram NMF protocol

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An ensemble based incremental learning framework for concept drift and class imbalance

An ensemble based incremental learning framework for concept...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Gregory Ditzler Robi Polikar ECE Department Signal Processing & Pattern Recognition Laboratory Rowan University Glassboro NY USA

We have recently introduced an incremental learning algorithm, Learn ++ .NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems under different drift scenarios. However, Learn ++ .NSE relies on error to weigh the classifiers in the ensemble on the most recent data. For balanced class distributions, this approach works very well, but when faced with imbalanced data, error is no longer an acceptable measure of performance. On the other hand, the well-established SMOTE algorithm can address the class imbalance issue, however, it cannot learn in nonstationary environments. While there is some literature available for learning in nonstationary environments and imbalanced data separately, the combined problem of learning from imbalanced data coming from nonstationary environments is underexplored. Therefore, in this work we propose two modified frameworks for an algorithm that can be used to incrementally learn from imbalanced data coming from a nonstationary environment.

关键词： Classification algorithms Algorithm design and analysis Heuristic algorithms Measurement Data models Bagging Training

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Annealed SMC Samplers for Dirichlet Process Mixture Models

Annealed SMC Samplers for Dirichlet Process Mixture Models

引用

International Conference on pattern recognition

作者： Yener Ulker Bilge Gunsel Ali Taylan Cemgil Multimedia Signal Proc.& Pattern Recognition Laboratory Department of Electronics and Communications Engineering Istanbul Technical University Istanbul Turkey Department of Computer Engineering Bogazici University Istanbul Turkey

ISBN: (纸本)9781424475421

In this work we propose a novel algorithm that approximates sequentially the Dirichlet Process Mixtures (DPM) model posterior. The proposed method takes advantage of the Sequential Monte Carlo (SMC) samplers framework to design an effective annealing procedure that prevents the algorithm to get trapped in a local mode. We evaluate the performance in a Bayesian density estimation problem with unknown number of components. The simulation results suggest that the proposed algorithm represents the target posterior much more accurately and provides significantly smaller Monte Carlo error when compared to particle filtering.

关键词： Kernel Monte Carlo methods Annealing Approximation methods Inference algorithms Markov processes Algorithm design and analysis

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Incremental learning in nonstationary environments with controlled forgetting

Incremental learning in nonstationary environments with cont...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ryan Elwell Robi Polikar Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering Department Rowan University Glassboro NJ USA

We have recently introduced an incremental learning algorithm, called Learn ++ .NSE, designed for Non-Stationary Environments (concept drift), where the underlying data distribution changes over time. With each dataset drawn from a new environment, Learn ++ .NSE generates a new classifier to form an ensemble of classifiers. The ensemble members are combined through a dynamically weighted majority voting, where voting weights are determined based on classifiers' age-adjusted accuracy on current and past environments. Unlike other ensemble-based concept drift algorithms, Learn ++ .NSE does not discard prior classifiers, allowing potentially cyclical environments to be learned more effectively. While Learn ++ .NSE has been shown to work well on a variety of concept drift problems, a potential shortcoming of this approach is the cumulative nature of the ensemble size. In this contribution, we expand our analysis of the algorithm to include various ensemble pruning methods to introduce controlled forgetting. Error or age-based pruning methods have been integrated into the algorithm to prevent potential out-voting from irrelevant classifiers or simply to save memory over an extended period of time. Here, we analyze the tradeoff between these precautions and the desire to handle recurring contexts (cyclical data). Comparisons are made using several scenarios that introduce various types of drift.

关键词： Neutron spin echo Algorithm design and analysis Voting

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ERP based decision fusion for AD diagnosis across cohorts

ERP based decision fusion for AD diagnosis across cohorts

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Metin Ahiskali Deborah Green John Kounios Christopher M. Clark Robi Polikar Signal Processing and Pattern Recognition Laboratory Department of Electrical and Computer Engineering Rowan University Glassboro NJ USA Department of Psychology Drexel University Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

As the average life expectancy increases, particularly in developing countries, prevalence of neurodegenerative diseases has also increased. This trend is especially alarming for Alzheimer's disease (AD); as there is no cure to stop or reverse the effects of AD. However, recent pharmacological advances can slow the progression of AD, but only if AD is diagnosed at early stages. We have previously introduced an ensemble of classifiers based approach for combining event related potentials obtained from different electrode locations as an effective approach for early diagnosis of AD. We further expand this approach and analyze its robustness and stability in two ways: comparing the diagnostic accuracy on hand selected and cleaned data vs. standard automated preprocessing, but more importantly, comparing the diagnostic accuracy on two different cohorts, whose data are collected under different settings: a research university lab and a community clinic.

关键词： Enterprise resource planning Proteins Biomedical signal processing Hospitals Alzheimer's disease Electrodes Robust stability Medical diagnostic imaging Biomarkers Electroencephalography

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Istanbul technical university at TRECVID 2008

Istanbul technical university at TRECVID 2008

引用

TREC Video Retrieval Evaluation, TRECVID 2008

作者： Gursoy, O. Gunsel, B. Multimedia Signal Processing and Pattern Recognition Group Department of Electronics and Communications Engineering Istanbul Technical University Maslak/Istanbul 34496 Turkey

ITU MSPR Group participates the TREC Video Retrieval Evaluation (TRECVID) in Content Based Copy Detection (CBCD) task. The system proposed by ITU MSPR consists of two main modules: Extraction of video fingerprints and search/retrieval. We propose a feature extraction scheme based on the Nonnegative Matrix Factorization(NMF)[1], which is an efficient dimension reduction technique in video processing[2]. Video fingerprint generation module takes the factorization matrices generated by NMF as its input and converts them to binary hashes by differencial coding. Extracted hashes are indexed into a database. Searching module first applies a hash matching procedure to locate potential matching points. It is followed by temporal merging that eliminates false alarms while combining subsegments. Initial results are promising for insertion of pattern, reencoding, blurring, change of gamma and noise addition. Future work will include impoving the current results and searching for robustness to geometric transformations such as shift, crop, flip and picture-in-picture.

关键词： Matrix algebra

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A multiple model structure for tracking by variable rate particle filters

A multiple model structure for tracking by variable rate par...

引用

作者： Ulker, Yener Gunsel, Bilge Kirbiz, Serap Multimedia Signal Processing and Pattern Recognition Lab. Istanbul Technical University Dep. of Electronics and Communications Engineering 34469 Maslak Istanbul Turkey

ISBN: (纸本)9781424421756

In contrast to the fixed rate modeling of the conventional methods, recently introduced variable rate particle filters (VRPF) achieves to track maneuvering objects with a small number of states by imposing a probability distribution on state arrival times. Although this enables VRPF an appealing method, representing the target motion dynamics with a single model hinders the capability of estimating maneuver parameters precisely. To overcome this weakness we have incorporated multiple model approach with the variable rate model structure. The introduced model referred as Multiple Model Variable Rate Particle Filter (MM-VRPF) utilizes a parsimonious representation for smooth regions of trajectory while it adaptively locates frequent state points at high maneuver regions, resulting in a much more accurate tracking. Simulation results obtained in a bearings-only target tracking problem show that the proposed model outperforms the conventional VRPF, the fixed rate multiple model particle filters (MMPF) and interacting multiple model using extended Kalman filters (IMM-EKF). © 2008 IEEE.

关键词： Extended Kalman filters

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A multiple model structure for tracking by variable rate particle filters

A multiple model structure for tracking by variable rate par...

引用

International Conference on pattern recognition

作者： Yener Ulker Bilge Gunsel Serap Kirbiz Department of Electronics and Communications Engineering Multimedia Signal Processing and Pattern Recognition Laboratory Istanbul Technical University Istanbul Turkey

关键词： Particle tracking Particle filters Target tracking Trajectory Motion estimation State estimation Filtering signal processing pattern recognition Probability distribution

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Iris recognition using Steerable Pyramids

Iris Recognition using Steerable Pyramids

引用

Workshops on Image processing Theory, Tools and Applications, IPTA

作者： Nefissa Khiari Hela Mahersia Kamel Hamrouni Laboratory of Signal Processing Image Processing and Pattern Recognition National Engineering School of Tunis University of Tunis Tunis Tunisia

This work presents a new iris recognition method based on steerable pyramid transform. This method consists of four steps: localization, normalization, features extraction and matching. After locating the iris boundaries by Hough Transform, normalization is operated by unwrapping the circular ring and isolating the noisy regions. Steerable pyramid filters are then used to capture orientation details from the iris texture. The features are extracted on each filtered sub-image to form a fixed length feature vector which will be compared to other vectors in the matching step. This technique has been tested on infrared light iris images. It has been compared, in both identification and verification modes, to known methods.

关键词： Iris recognition Feature extraction Wavelet transforms Image processing Matched filters Gabor filters Filtering Biometrics Eyelids Eyelashes

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Incremental clustering via nonnegative matrix factorization

Incremental clustering via nonnegative matrix factorization

引用

International Conference on pattern recognition

作者： Serhat Selcuk Bucak Bilge Gunsel Electrical-Electronics Eng. Faculty Department of Electronics and Communications EngineeringMultimedia Signal Processing and Pattern Recognition Laboratory Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781424421749

Nonnegative matrix factorization (NMF) has been shown to be an efficient clustering tool. However, NMF¿s batch nature necessitates recomputation of whole basis set for new samples. Although NMF is a powerful content representation tool, this limits the use of NMF in online processing of large data sets. Another problem with NMF, like other partitional methods, is determining the actual number of clusters. Deciding the rank of the factorization is also critical since it has a significant effect on clustering performance. This paper introduces an NMF based incremental clustering algorithm which allows increasing number of clusters adaptively thus eliminates optimal rank selection problem. Test results obtained on large video data sets demonstrate that the proposed clustering scheme is capable of labeling linearly separable data as well as non-separable samples with a small false positive ratio.

关键词： Clustering algorithms Clustering methods Encoding signal processing pattern recognition Power engineering and energy Partitioning algorithms signal processing algorithms Testing Labeling

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