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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51-60 订阅

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Photorealistic 3D Mapping of Indoors by RGB-D Scanning Process

Photorealistic 3D Mapping of Indoors by RGB-D Scanning Proce...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Tommi Tykkala Andrew I. Comport Joni-Kristian Kamarainen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Kouvola Unit Finland CNRS-I3S/University of Nice Sophia-Antipolis France Department of Signal Processing Tampere University of Technology Finland

ISBN: (纸本)9781467363563

In this work, a RGB-D input stream is utilized for GPU-boosted 3D reconstruction of textured indoor environments. The goal is to develop a process which produces standard 3D models from indoors to explore them virtually. Camera motion is tracked in 3D space by registering the current view with a reference view. Depending on the trajectory shape, the reference is either fetched from a concurrently built keyframe model or from a previous RGB-D measurement. Real-time tracking (30Hz) is executed on a low-end GPU, which is possible because structural data is not fused concurrently. After camera poses have been estimated, both trajectory and structure are refined in post-processing. The global point cloud is compressed into a watertight polygon mesh by using Poisson reconstruction method. The Poisson method is well-suited, because it compressed the raw data without introducing multiple geometries and also fills holes efficiently. Holes are typically introduced at occluded regions. Texturing is generated by backprojecting the nearest RGB image onto the mesh. The final model is stored in a standard 3D model format to allow easy user exploration and navigation in virtual 3D environment.

关键词： trajectory three-dimensional displays Photorealistic Three Dimensions INDOORS navigation Sim'eon Denis Poisson

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Semi-Supervised Learning in Initially Labeled Non-Stationary Environments with Gradual Drift

Semi-Supervised Learning in Initially Labeled Non-Stationary...

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International Joint Conference on Neural Networks

作者： Karl B. Dyer Robi Polikar Signal Processing & Pattern Recognition Laboratory Rowan University

ISBN: (纸本)9781467314886

Semi-supervised learning (SSL) in non-stationary environments has received relatively little attention in machine learning, despite a growing number of applications that can benefit from a properly configured SSL algorithm. Previous works in learning non-stationary data have analyzed such cases where both labeled and unlabeled instances are received at every time step and/or in regular intervals;however, to the best of our knowledge, no work has investigated the case where labeled instances are received only at the initial time step, followed by unlabeled instances provided in subsequent time steps. In this proof-of-concept work, we propose a new framework for learning in a non-stationary environment that provides only unlabeled data after the initial time step, to which we refer to as initially labeled environment. The proposed framework generates labels for previously unlabeled data at each time step to be combined with incoming unlabeled data - possibly from a drifting distribution - using a compacted polytope sample extraction algorithm. We have conducted two experiments to demonstrate the feasibility and reliability of the approach. This proof-of-concept is presented in two dimensions;however, the algorithm can be extended to higher dimensions with appropriate modifications.

关键词： Alpha shape Concept drift Non-stationary environment Shape offsets Semi-supervised learning

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Perceptually weighted Non-negative Matrix Factorization for blind single-channel music source separation

Perceptually weighted Non-negative Matrix Factorization for ...

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International Conference on pattern recognition

作者： S. Kırbız B. Gunsel Multimedia Signal Processing and Pattern Recognition Laboratory Department of Electronics and Communications Engineering Technical University Maslak Istanbul Turkey

We propose a blind single-channel musical source separation method that improves perceptual quality of the separated sources. It uses the advantages of subspace learning based on Non-negative Matrix Factor 2-D Deconvolution (NMF2D). To improve the perceptual quality of separation, we propose a weighted divergence type cost function for the optimization that adopts the auditory model defined in ITU-R BS.1387 into the source separation. It is shown that the proposed perceptually weighted NMF2D scheme efficiently clusters the bases of subspace representation corresponding to notes generated by single instruments. Source separation performance has been reported on musical mixtures resulting an improvement in perceptual quality measures.

关键词： Source separation Spectrogram Time frequency analysis Instruments Humans Time domain analysis Computational complexity

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Analysis of Complexity Based EEG Features for the Diagnosis of Alzheimer's Disease

Analysis of Complexity Based EEG Features for the Diagnosis ...

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

作者： Tyler Staudinger Robi Polikar Signal Processing and Pattern Recognition Laboratory Dept. of Electrical and Computer Eng. at Rowan University

ISBN: (纸本)9781424441211

As life expectancy increases, particularly in the developed world, so does the prevalence of Alzheimer's Disease (AD). AD is a neurodegenerative disorder characterized by neurofibrillary plaques and tangles in the brain that leads to neuronal death and dementia. Early diagnosis of AD is still a major unresolved health concern: several biomarkers are being investigated, among which the electroencephalogram (EEG) provides the only option for an electrophysiological information. In this study, EEG signals obtained from 161 subjects - 79 with AD, and 82 age-matched controls (CN) - are analyzed using several nonlinear signal complexity measures. These measures include: Higuchi fractal dimension (HFD), spectral entropy (SE), spectral centroid (SC), spectral roll-off (SR), and zero-crossing rate (ZCR). HFD is a quantitative measure of time series complexity derived from fractal theory. Among spectral measures, SE measures the level of disorder in the spectrum, SC is a measure of spectral shape, and SR is frequency sample below which a specified percent of the spectral magnitude distribution is contained. Lastly, ZCR is simply the rate at which the signal changes signs. A t-test was first applied to determine those features that provide significant differences between the groups. Those features were then used to train a neural network. The classification accuracies ranged from 60-66%, suggesting they contain some discriminatory information;however, not enough to be clinically useful alone. Combining these features and training a support vector machine (SVM) resulted in a diagnostic accuracy of 78%, indicating that these feature carry complementary information.

关键词： Alzheimer's disease EEG Higuchi Fractal Dimension Spectral Entropy Spectral Centroid Spectral Roll-Off Zero-Crossing Rate

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Semi-supervised learning in nonstationary environments

Semi-supervised learning in nonstationary environments

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International Joint Conference on Neural Networks (IJCNN)

作者： Gregory Ditzler Robi Polikar Signal Processing and Pattern Recognition Laboratory in the Electrical & Computer Engineering Department Rowan University Glassboro NJ USA

Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. These applications are usually associated with streaming data, either online or in batches, and concept drift algorithms are trained to detect and track the drifting concepts. While concept drift itself is a significantly more complex problem than the traditional machine learning paradigm of data coming from a fixed distribution, the problem is further complicated when obtaining labeled data is expensive, and training must rely, in part, on unlabelled data. Independently from concept drift research, semi-supervised approaches have been developed for learning from (limited) labeled and (abundant) unlabeled data; however, such approaches have been largely absent in concept drift literature. In this contribution, we describe an ensemble of classifiers based approach that takes advantage of both labeled and unlabeled data in addressing concept drift: available labeled data are used to generate classifiers, whose voting weights are determined based on the distances between Gaussian mixture model components trained on both labeled and unlabeled data in a drifting environment.

关键词： Classification algorithms Data models Clustering algorithms Training data Training Testing Algorithm design and analysis

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Color constancy and non-uniform illumination: Can existing algorithms work?

Color constancy and non-uniform illumination: Can existing a...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Michael Bleier Christian Riess Shida Beigpour Eva Eibenberger Elli Angelopoulou Tobias Tröger André Kaup Pattern Recognition Lab University of Erlangen-Nuremberg Germany Computer Vision Center Universidad Autónoma de Barcelona Spain Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany

The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

关键词： Image color analysis Lighting Databases Light sources Image segmentation Estimation Bayesian methods

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Video vehicle detection through multiple background-based features and statistical learning

Video vehicle detection through multiple background-based fe...

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International Conference on Intelligent Transportation

作者： Mingxia Sun Kunfeng Wang Ming Tang Fei-Yue Wang Jinfeng Yang Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences (CAS) Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) Beijing China

This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.

关键词： Vehicles Feature extraction Training Image edge detection Vehicle detection Meteorology Radio frequency

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ITU MSPR TRECVID 2010 video copy detection system

ITU MSPR TRECVID 2010 video copy detection system

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TREC Video Retrieval Evaluation, TRECVID 2010

作者： Kutluk, Sezer Gunsel, Bilge Multimedia Signal Processing and Pattern Recognition Group Department of Electronics and Communications Engineering Istanbul Technical University Maslak/Istanbul 34496 Turkey

In this paper we describe the system designed by the ITU MSPR Group for content based video fingerprinting as applied to the TRECVID 2010 Content Based Copy Detection (CBCD) benchmark. This year focus of the system was on integration of audio and video fingerprinting to improve the robustness to attacks. The proposed system consists of three main modules: Audio/video fingerprint extraction, audio/video search and retrieval, and audiovisual decision fusion. We propose a video feature extraction scheme based on the Nonnegative Matrix Factorization (NMF) which is an efficient dimension reduction technique in video processing. Video fingerprint generation module takes the factorization matrices generated by NMF as its input and converts them to binary hashes by differencial coding [1, 2]. For audio data we perform an audio fingerprinting method that is similar to the one proposed in [3]. Extracted audio and video hashes are indexed into a database. Searching module first applies a hash matching procedure to locate potential matching points both in audio and video. This is followed by decision fusion that eliminates false alarms and finalizes the matching and retrieval.

关键词： Matrix algebra

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Optimal ν-SVM parameter estimation using multi objective evolutionary algorithms

Optimal ν-SVM parameter estimation using multi objective ev...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

作者： Ethridge, James Ditzler, Gregory Polikar, Robi Dept. of Electrical and Computer Engineering Rowan University Signal Processing and Pattern Recognition Laboratory Glassboro NJ 08028 United States

ISBN: (纸本)9781424469109

Using a machine learning algorithm for a given application often requires tuning design parameters of the classifier to obtain optimal classification performance without overfitting. In this contribution, we present an evolutionary algorithm based approach for multi-objective optimization of the sensitivity and specificity of a ν-SVM. The ν-SVM is often preferred over the standard C-SVM due to smaller dynamic range of the ν parameter compared to the unlimited dynamic range of the C parameter. Instead of looking for a single optimization result, we look for a set of optimal solutions that lie along the Pareto optimality front. The traditional advantage of using the Pareto optimality is of course the flexibility to choose any of the solutions that lies on the Pareto optimality front. However, we show that simply maximizing sensitivity and specificity over the Pareto front leads to parameters that appear to be mathematically optimal yet still cause overfitting. We propose a multiple objective optimization approach with three objective functions to find additional parameter values that do not cause overfitting. © 2010 IEEE.

关键词： Multiobjective optimization

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Key-frame based video fingerprinting by NMF

Key-frame based video fingerprinting by NMF

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IEEE International Conference on Image processing

作者： Ozgun Cirakman Bilge Gunsel N.Serap Sengor Ozan Gursoy Multimedia Signal Processing and Pattern Recognition Laboratory Department of Electronics and Communication Engineering Istanbul Technical University Maslak Istanbul Turkey

This paper presents a key-frame based video fingerprinting method in which the fingerprint matching is modeled as a two hypothesis testing problem. The perceptual fingerprints that uniquely identify the video content are extracted by non-negative matrix factorization (NMF) via Gaussian weighting in order to assure compactness and robustness to global luminance distortions. The system performance is further improved to enhance its robustness to geometric attacks by integrating the transform invariant NMF (T-NMF) indicies into the matching scheme. The overall performance is evaluated on TRECVID video sequences. It is shown that the proposed video fingerprinting method is highly robust to global attacks described by TRECVID and it can also handle the geometric attacks for the transformations used at indexing phase.

关键词： Decision support systems Image processing Discrete wavelet transforms Conferences

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