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AN EASY-TO-USE IMAGE LABELING PLATFORM FOR AUTOMATIC MAGNETIC RESONANCE IMAGE QUALITY ASSESSMENT

AN EASY-TO-USE IMAGE LABELING PLATFORM FOR AUTOMATIC MAGNETI...

引用

International Symposium on Biomedical Imaging

作者： Thomas Küstner Philip Wolf Martin Schwartz Annika Liebgott Fritz Schick Sergios Gatidis Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Section on Experimental Radiology Universtity of Tubingen Department of Radiology Universtity of Tubingen

ISBN: (纸本)9781509011735

In medical imaging, images are usually evaluated by a human observer (HO) depending on the underlying diagnostic question which can be a time-demanding and cost-intensive process. Model observers (MO) which mimic the human visual system can help to support the HO during this reading process or can provide feedback to the MR scanner and/or HO about the derived image quality. For this purpose MOs are trained on HO-derived image labels with respect to a certain diagnostic task. We propose a non-reference image quality assessment system based on a machine-learning approach with a deep neural network and active learning to keep the amount of needed labeled training data small. A labeling platform is developed as a web application with accounted data security and confidentiality to facilitate the HO labeling procedure. The platform is made publicly available.

关键词： magnetic resonance non-reference image quality assessment image labeling machine-learning deep neural network

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Direction-of-Arrival Estimation and Cramer-Rao Bound for Multi-Carrier MIMO Radar

Direction-of-Arrival Estimation and Cramer-Rao Bound for Mul...

引用

European signal processing Conference

作者： Michael Ulrich Kilian Rambach Bin Yang Institute of Signal Processing and System Theory University of Stuttgart

ISBN: (纸本)9781509018918

Multi-carrier (MC) multiple-input multiple-output (MIMO) radar was recently applied to build sparse virtual arrays with a large aperture for a high-accuracy direction-of-arrival (DOA) estimation. The resulting grating lobes (DOA ambiguities) were resolved using multiple carriers. One problem of MC-MIMO is the coupling of the unknown parameters range and DOA. In this contribution, we study this range-DOA coupling for MC-MIMO systems. We consider both Cramer-Rao bound (CRB) of these parameters and their estimation. We show that a suitable choice of the coordinate system decouples range and DOA parameters in both CRB and estimation. This enables a sequential range and DOA estimation instead of a more complex joint estimation. Explanations of this phenomenon are given and simulation results confirm the theoretical findings.

关键词： Direction-of-arrival estimation Estimation Radar Couplings Radar antennas MIMO Direction of arrival estimation Radar antennas multiple input multiple output systems Couplings radar complementary return to bias United States Department of Agriculture coupling MIMO radar Cramer-Rao bounds wide-aperture

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Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime 19

Detecting the dimension of the subspace correlated across mu...

引用

19th IEEE Statistical signal processing Workshop, SSP 2016

作者： Hasija, Tanuj Song, Yang Schreier, Peter J. Ramirez, David Signal and System Theory Group Universität Paderborn Germany Signal Processing Group University Carlos III of Madrid Leganés Spain Gregorio Maranõn Health Research Institute Madrid Spain

ISBN: (纸本)9781467378024

This paper addresses the problem of detecting the number of signals correlated across multiple data sets with small sample support. While there have been studies involving two data sets, the problem with more than two data sets has been less explored. In this work, a rank-reduced hypothesis test for more than two data sets is presented for scenarios where the number of samples is small compared to the dimensions of the data sets. © 2016 IEEE.

关键词： Bartlett statistic canonical correlation analysis hypothesis testing multiple data sets Small sample support

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On semantic image segmentation using deep convolutional neural network with shortcuts and easy class extension

On semantic image segmentation using deep convolutional neur...

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Workshops on Image processing theory, Tools and Applications, IPTA

作者： Chunlai Wang Lukas Mauch Ze Guo Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Germany

ISBN: (纸本)9781467389112

In this paper we examine the use of deep convolutional neural networks for semantic image segmentation, which separates an input image into multiple regions corresponding to predefined object classes. We use an encoder-decoder structure and aim to improve it in convergence speed and segmentation accuracy by adding shortcuts between network layers. Besides, we investigate how to extend an already trained model to other new object classes. We propose a new strategy for class extension with only little training data and class labels. In the experiments we use two street scene datasets to demonstrate the strength of shortcuts, to study the contextual information encoded in the learned model and to show the effectiveness of our class extension method.

关键词： Image segmentation Training Semantics Neural networks Computer architecture Tensile stress Convolution

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An unsupervised object-level image segmentation method based on foreground and background priors

An unsupervised object-level image segmentation method based...

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IEEE Southwest Symposium on Image Analysis and Interpretation

作者： Chunlai Wang Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Germany

Conventional unsupervised image segmentation methods return many superpixels or object parts and thus tend to over-segmentation. In this paper, we present a novel post-processing approach for unsupervised object-level image segmentation (UnOLIS). Starting with the results of any conventional unsupervised segmentation method, we first combine a global region-based saliency and a robust background feature to cluster the pre-segmented regions into foreground and background. We then design a region growing process, encoded with several object priors, to generate a high quality foreground object segmentation. In parallel, we group the background regions into different stuffs by clustering. We test our method on the Berkeley Segmentation Dataset (BSDS500). Our approach significantly improves conventional unsupervised segmentation methods and achieves almost comparable results as the state-of-the-art supervised image segmentation methods.

关键词： Image segmentation Image color analysis Image edge detection Robustness Training Organizations Measurement

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A NOVEL DNN-HMM-BASED APPROACH FOR EXTRACTING SINGLE LOADS FROM AGGREGATE POWER signalS

A NOVEL DNN-HMM-BASED APPROACH FOR EXTRACTING SINGLE LOADS F...

引用

IEEE International Conference on Acoustics, Speech and signal processing

作者： Lukas Mauch Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Germany

ISBN: (纸本)9781479999897

This paper presents a new supervised approach to extract the power trace of individual loads from single channel aggregate power signals in non-intrusive load monitoring (NILM) systems. Recent approaches to this source separation problem are based on factorial hidden markov models (FHMM). Drawbacks are the needed knowledge of HMM models for all loads, what is infeasible for large buildings, and the large combinatorial complexity. Our approach trains HMM with two emission probabilities, one for the single load to be extracted and the other for the aggregate power signal. A Gaussian distribution is used to model observations of the single load whereas observations of the aggregate signal are modeled with a Deep Neural Network (DNN). By doing so, a single load can be extracted from the aggregate power signal without knowledge of the remaining loads. The performance of the algorithm is evaluated on the Reference Energy Disaggregation (REDD) dataset.

关键词： Non-intrusive load monitoring (NILM) supervised power disaggregation Hidden Markov Model (HMM) Deep Neural Networks (DNN) single load LOAD aggregates Hidden Markov models signals Source separation Factorial Hidden Markov Model Aggregates Energy sources Power Power Gaussian distribution

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Fusion of stereo camera and MIMO-FMCW radar for pedestrian tracking in indoor environments

Fusion of stereo camera and MIMO-FMCW radar for pedestrian t...

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International Conference on Information Fusion

作者： Roman Streubel Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Germany

In this paper, we present a fusion of stereo camera and radar targets to significantly improve the tracking of pedestrians indoors especially suited for surveillance and security applications. Indoor environments pose bigger challenges for pedestrian tracking compared to outdoor environments. This makes pedestrian tracking with only a camera or radar difficult. In this work, we demonstrate that in some preliminary experiments our fusion system increases the multiple object tracking accuracy (MOTA) from -16.7% and 38.0% for camera or radar only tracking to 90.9% using a fusion of both. Furthermore, the proposed fusion system achieves a multiple object tracking precision (MOTP 3D ) of 90.3% compared to 83.4% and 81.8% obtained via camera and radar only tracking, respectively.

关键词： Radar tracking Cameras Radar cross-sections Radar signal processing Target tracking Estimation

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Saliency-guided object proposal for refined salient region detection

Saliency-guided object proposal for refined salient region d...

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IEEE Visual Communications and Image processing (VCIP)

作者： Chunlai Wang Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Germany

ISBN: (纸本)9781509053179

Automatic detection of visually salient regions across images is useful in many applications. Traditional methods predict saliency values of pixels in a bottom-up fashion and use low-level features. Recent researches demonstrate that high-level information are also important for salient region detection. In this paper, we propose a novel approach of integrating object-level information and bottom-up saliency model. By using saliency-guided object proposals, we implicitly remove the noisy salient regions and produce a refined saliency map. We experimentally show that our approach boosted the performance of bottom-up saliency models and performs favorably against the state-of-the-art methods.

关键词： Proposals Image segmentation Image color analysis Search problems Databases Visualization Face

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Multi-carrier MIMO radar: A concept of sparse array for improved DOA estimation

Multi-carrier MIMO radar: A concept of sparse array for impr...

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IEEE National Conference on Radar

作者： Michael Ulrich Bin Yang Institute for Signal Processing and System Theory University of Stuttgart Stuttgart Germany

In this paper we propose a multi-carrier (MC) based sparse array for improved direction-of-arrival (DOA) estimation. We use a spatial (physical or virtual) array of a small number of antennas, but with a large aperture to achieve a high DOA estimation accuracy. The resulting problem of grating lobes (spatial aliasing) is addressed by using multiple carrier frequencies. In contrast to the single-carrier case, by a suitable choice of multiple carrier frequencies the resulting antenna position-to-wavelength ratios can be used to satisfy the spatial anti-aliasing condition. We present such design rules in this paper. A new problem, however, is that a target range results in different phase changes for different carriers. This is the MC range-DOA coupling. We both discuss the joint range-DOA deterministic maximum likelihood estimation and a simplified DOA estimation algorithm with range elimination. Their performance in simulations are compared to the Cramer-Rao bound.

关键词： Direction-of-arrival estimation Estimation Antenna arrays Radar Radar antennas Gratings

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A Combined Partial Volume Reduction and Super-resolution Reconstruction for Magnetic Resonance Images

A Combined Partial Volume Reduction and Super-resolution Rec...

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IEEE Nuclear Science Symposium

作者： Faezeh Fallah Bin Yang Fritz Schick Fabian Bamberg Department of diagnostic and interventional radiology University of Tübingen Germany Institute of signal processing and system theory University of Stuttgart Stuttgart Germany Institute of signal processing and system theory University of Stuttgart Germany

Magnetic resonance imaging provides a superior soft tissue contrast and a noninvasive means for automatic diagnosis of tissue pathogenesis. However, like most imaging modalities, it suffers from a compromise between the achievable spatial resolution, scan time efficiency, and signal-to-noise-ratio. To address this difficulty, super-resolution techniques have been proposed to enhance the spatial resolution of images in the post-acquisition steps. Most of those methods are proposed for nonmedical images. Thus, they do not consider the specific requirements of medical imaging in respect of data fidelity. In the present work, we propose a novel approach for super-resolution estimation that simultaneously reduces partial volume effects in order to enhance the edges without introducing artefactual effects to medical images. In this method, instead of using an edge-preserving preconditioner an interpolation based on the reverse diffusion process of material has been incorporated into the iterative estimation of images of higher spatial resolution. The proposed scheme outperforms the edge-preserving preconditioner in terms of image fidelity and speed of estimation.

关键词： signal resolution Spatial resolution Estimation Imaging Image edge detection signal to noise ratio

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