| 1. |
- Ali, Muhaddisa Barat, 1986, et al.
(author)
-
Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification
- 2019
-
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11678 LNCS, s. 234-245
-
Conference paper (peer-reviewed)abstract
- Diagnosis and timely treatment play an important role in preventing brain tumor growth. Deep learning methods have gained much attention lately. Obtaining a large amount of annotated medical data remains a challenging issue. Furthermore, high dimensional features of brain images could lead to over-fitting. In this paper, we address the above issues. Firstly, we propose an architecture for Generative Adversarial Networks to generate good quality synthetic 2D MRIs from multi-modality MRIs (T1 contrast-enhanced, T2, FLAIR). Secondly, we propose a deep learning scheme based on 3-streams of Convolutional Autoencoders (CAEs) followed by sensor information fusion. The rational behind using CAEs is that it may improve glioma classification performance (as comparing with conventional CNNs), since CAEs offer noise robustness and also efficient feature reduction hence possibly reduce the over-fitting. A two-round training strategy is also applied by pre-training on GAN augmented synthetic MRIs followed by refined-training on original MRIs. Experiments on BraTS 2017 dataset have demonstrated the effectiveness of the proposed scheme (test accuracy 92.04%). Comparison with several exiting schemes has provided further support to the proposed scheme.
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| 2. |
- Alipoor, Mohammad, 1983, et al.
(author)
-
A Novel Framework for repeated measurements in diffusion tensor imaging
- 2016
-
In: 3rd (ACM) Int'l Conf. on Biomedical and Bioinformatics Engineering (ICBBE 2016). - New York, NY, USA : ACM. - 9781450348249 ; Part F125793, s. 1-6
-
Conference paper (peer-reviewed)abstract
- In the context of diffusion tensor imaging (DTI), the utility of making repeated measurements in each diffusion sensitizing direction has been the subject of numerous stud-ies. One can estimate the true signal value using either the raw complex-valued data or the real-valued magnitudesignal. While conventional methods focus on the former strategy, this paper proposes a new framework for acquiring/processing repeated measurements based on the latter strategy. The aim is to enhance the DTI processing pipeline by adding a diffusion signal estimator (DSE). This permits us to exploit the knowledge of the noise distribution to estimate the true signal value in each direction. An extensive study of the proposed framework, including theoretical analysis, experiments with synthetic data, performance evaluation and comparisons is presented.Our results show that the precision of estimated diffusionparameters is dependent on the number of available samplesand the manner in which the DSE accounts for noise. Theproposed framework improves the precision in estimationof diffusion parameters given a sufficient number of uniquemeasurements. This encourages future work with rich realdatasets and downstream applications.
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| 3. |
- Alipoor, Mohammad, 1983, et al.
(author)
-
Determinant of the information matrix: a new rotation invariant optimality metric to design gradient encoding schemes
- 2015
-
In: 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 16-19 April 2015. - 1945-8452. - 9781479923748 ; 2015-July, s. 462-465
-
Conference paper (peer-reviewed)abstract
- Minimum condition number (CN) gradient encoding schemewas introduced to diffusion MRI community more than adecade ago. It’s computation requires tedious numerical optimization which usually leads to sub-optimal solutions. TheCN does not reflect any benefits in acquiring more measurements, i.e. it’s optimal value is constant for any numberof measurements. Further, it is variable under rotation. Inthis paper we (i) propose an accurate method to computeminimum condition number scheme; and (ii) introduce determinant of the information matrix (DIM) as a new optimality metric that scales with number of measurements anddoes reflect what one would gain from acquiring more measurements. Theoretical analysis shows that DIM is rotationinvariant. Evaluations on state-of-the-art encoding schemesproves the relevance and superiority of the proposed metriccompared to condition number.
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| 4. |
- Alipoor, Mohammad, 1983, et al.
(author)
-
Fourth order tensor-based diffusion MRI signal modeling
- 2015
-
In: International symposium on biomedical imaging, White Matter Modeling Challenge. 16-19 April 2015, New York, USA..
-
Conference paper (other academic/artistic)abstract
- This abstract describes forth order tensor-based diffusion signal modeling as proposed in [1].
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| 5. |
- Alipoor, Mohammad, 1983, et al.
(author)
-
Icosahedral gradient encoding scheme for an arbitrary number of measurements
- 2015
-
In: International symposium on biomedical imaging. - 1945-8452. - 9781479923748 ; 2015-July, s. 959-962
-
Conference paper (peer-reviewed)abstract
- The icosahedral gradient encoding scheme (GES) is widelyused in diffusion MRI community due to its uniformly distributed orientations and rotationally invariant condition number. The major drawback with this scheme is that it is notavailable for arbitrary number of measurements. In this paper(i) we propose an algorithm to find the icosahedral schemefor any number of measurements. Performance of the obtained GES is evaluated and compared with that of Jones andtraditional icosahedral schemes in terms of condition number,standard deviation of the estimated fractional anisotropy anddistribution of diffusion sensitizing directions; and (ii) we introduce minimum eigenvalue of the information matrix as anew optimality metric to replace condition number. Unlikecondition number, it is proportional to the number of measurements and thus in agreement with the intuition that moremeasurements leads to more robust tensor estimation. Furthermore, it may independently be maximized to design GESsfor different diffusion imaging techniques.
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| 6. |
- Alipoor, Mohammad, 1983, et al.
(author)
-
K-Optimal Gradient Encoding Scheme for Fourth-Order Tensor-Based Diffusion Profile Imaging
- 2015
-
In: Biomed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141.
-
Journal article (peer-reviewed)abstract
- The design of an optimal gradient encoding scheme (GES) is a fundamental problem in diffusion MRI. It is well studied for the case of second-order tensor imaging (Gaussian diffusion). However, it has not been investigated for the wide range of non-Gaussian diffusion models. The optimal GES is the one that minimizes the variance of the estimated parameters. Such a GES can be realized by minimizing the condition number of the design matrix (K-optimal design). In this paper, we propose a new approach to solve the K-optimal GES design problem for fourth-order tensor-based diffusion profile imaging. The problem is a nonconvex experiment design problem. Using convex relaxation, we reformulate it as a tractable semidefinite programming problem. Solving this problem leads to several theoretical properties of K-optimal design: (i) the odd moments of the K-optimal design must be zero; (ii) the even moments of the K-optimal design are proportional to the total number of measurements; (iii) the K-optimal design is not unique, in general; and (iv) the proposed method can be used to compute the K-optimal design for an arbitrary number of measurements. Our Monte Carlo simulations support the theoretical results and show that, in comparison with existing designs, the K-optimal design leads to the minimum signal deviation.
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| 7. |
- Alipoor, Mohammad, 1983, et al.
(author)
-
Optimal Experiment Design for Mono-Exponential Model Fitting: Application to Apparent Diffusion Coefficient Imaging
- 2015
-
In: BioMed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141. ; 2015
-
Journal article (peer-reviewed)abstract
- The mono-exponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics.The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for mono-exponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (?-optimal design). In contrast to previous methods, ?-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, range of ?-values). Using Monte Carlo simulations we show that the ?-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.
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| 8. |
- Alipoor, Mohammad, 1983, et al.
(author)
-
Optimal Gradient Encoding Schemes for Diffusion Tensor and Kurtosis Imaging
- 2016
-
In: IEEE transactions on Computational Imaging. - 2333-9403. ; 2:3, s. 375-391
-
Journal article (peer-reviewed)abstract
- Diffusion-derived parameters find application in characterizing pathological and developmental changes in living tissues. Robust estimation of these parameters is important because they are used for medical diagnosis. An optimal gradient encoding scheme (GES) is one that minimizes the variance of the estimated diffusion parameters. This paper proposes a method for optimal GES design for two diffusion models: high-order diffusion tensor (HODT) imaging and diffusion kurtosis imaging (DKI). In both cases, the optimal GES design problem is formulated as a D-optimal (minimum determinant) experiment design problem. Then, using convex relaxation, it is reformulated as a semidefinite programming problem. Solving these problems we show that: 1) there exists a D-optimal solution for DKI that is simultaneously D-optimal for second- and fourth-order diffusion tensor imaging (DTI); 2) the traditionally used icosahedral scheme is approximately D-optimal for DTI and DKI; 3) the proposed D-optimal design is rotation invariant; 4) the proposed method can be used to compute the optimal design ($b$ -values and directions) for an arbitrary number of measurements and shells; and 5) using the proposed method one can obtain uniform distribution of gradient encoding directions for a typical number of measurements. Importantly, these theoretical findings provide the first mathematical proof of the optimality of uniformly distributed GESs for DKI and HODT imaging. The utility of the proposed method is further supported by the evaluation results and comparisons with with existing methods.
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| 9. |
- Bagheri, Azam, et al.
(author)
-
A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification
- 2018
-
In: IEEE Transactions on Power Delivery. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8977 .- 1937-4208. ; 33:6, s. 2794-2802
-
Journal article (peer-reviewed)abstract
- This paper proposes a novel method for voltage dip classification using deep convolutional neural networks. The main contributions of this paper include: (a) to propose a new effective deep convolutional neural network architecture for automatically learning voltage dip features, rather than extracting hand-crafted features; (b) to employ the deep learning in an effective two-dimensional transform domain, under space-phasor model (SPM), for efficient learning of dip features; (c) to characterize voltage dips by two-dimensional SPM-based deep learning, which leads to voltage dip features independent of the duration and sampling frequency of dip recordings; (d) to develop robust automatically-extracted features that are insensitive to training and test datasets measured from different countries/regions.Experiments were conducted on datasets containing about 6000 measured voltage dips spread over seven classes measured from several different countries. Results have shown good performance of the proposed method: average classification rate is about 97% and false alarm rate is about 0.50%. The test results from the proposed method are compared with the results from two existing dip classification methods. The proposed method is shown to out-perform these existing methods.
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| 10. |
- Bagheri, Azam, et al.
(author)
-
Big data from smart grids
- 2018
-
In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017. - New York : Institute of Electrical and Electronics Engineers (IEEE). - 9781538619537
-
Conference paper (peer-reviewed)abstract
- This paper gives a general introduction to “Big Data” in general and to Big Data in smart grids in particular. Large amounts of data (Big Data) contains a lots of information, however developing the analytics to extract such information is a big challenge due to some of the particular characteristics of Big Data. This paper investigates some existing analytic algorithms, especially deep learning algorithms, as tools for handling Big Data. The paper also explains the potential for deep learning application in smart grids.
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| 11. |
- Bagheri, Azam, et al.
(author)
-
Generative Adversarial Model-Guided Deep Active Learning for Voltage Dip Labelling
- 2019
-
In: 2019 IEEE Milan PowerTech. - : IEEE.
-
Conference paper (peer-reviewed)abstract
- In many real applications, the ground truths of class labels from voltage dip sequences used for training a voltage dip classification system are unknown, and require manual labelling by human experts. This paper proposes a novel deep active learning method for automatic labelling of voltage dip sequences used for the training process. We propose a novel deep active learning method, guided by a generative adversarial network (GAN), where the generator is formed by modelling data with a Gaussian mixture model and provides the estimated probability distribution function (pdf) where the query criterion of the deep active learning method is built upon. Furthermore, the discriminator is formed by a support vector machine (SVM). The proposed method has been tested on a voltage dip dataset (containing 916 dips) measured in a European country. The experiments have resulted in good performance (classification rate 83% and false alarm 3.2%), which have demonstrated the effectiveness of the proposed method.
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| 12. |
- Bagheri, Azam, et al.
(author)
-
Improved characterization of multi-stage voltage dips based on the space phasor model
- 2018
-
In: Electric power systems research. - : Elsevier. - 0378-7796 .- 1873-2046. ; 154, s. 319-328
-
Journal article (peer-reviewed)abstract
- This paper proposes a method for characterizing voltage dips based on the space phasor model of the three phase-to-neutral voltages, instead of the individual voltages. This has several advantages. Using a K-means clustering algorithm, a multi-stage dip is separated into its individual event segments directly instead of first detecting the transition segments. The logistic regression algorithm fits the best single-segment characteristics to every individual segment, instead of extreme values being used for this, as in earlier methods. The method is validated by applying it to synthetic and measured dips. It can be generalized for application to both single- and multi-stage dips.
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| 13. |
- Balouji, Ebrahim, 1985, et al.
(author)
-
A LSTM-based Deep Learning Method with Application to Voltage Dip Classification
- 2018
-
In: 2018 18TH INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER (ICHQP). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2164-0610. - 9781538605172 - 9781538605172 ; 2018-May
-
Conference paper (peer-reviewed)abstract
- In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent Neural Networks (RNNs). A total of 5982 three-phase one-cycle voltage dip RMS sequences, measured from several countries, has been used in our experiments. Our results have shown that the proposedmethod is able to classify the voltage dips from learned features in LSTM, with 93.40% classification accuracy on the test data set. The developed architecture is shown to be novel for feature learning and classification of voltage dips. Different from the conventional machine learning methods, the proposed method is able to learn dip features without requiring transition-event segmentation, selecting thresholds, and using expert rules or human expert knowledge, when a large amount of measurement data is available. This opens a new possibility of exploiting deep learning technology for power quality data analytics and classification.
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| 14. |
- Bäckström, Karl, 1994, et al.
(author)
-
An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images
- 2018
-
Conference paper (peer-reviewed)abstract
- Automatic extraction of features from MRI brain scans and diagnosis of Alzheimer’s Disease (AD) remain a challenging task. In this paper, we propose an efficient and simple three dimensional convolutional network (3D ConvNet) architecture that is able to achieve high performance for detection of AD on a relatively large dataset. The proposed 3D ConvNet consists of five convolutional layers for feature extraction, followed by three fully-connected layers for AD/NC classification. The main contributions of the paper include: (a) propose a novel and effective 3D ConvNet architecture; (b) study the impact of hyper-parameter selection on the performance of AD classification; (c) study the impact of pre-processing; (d) study the impact of data partitioning; (e) study the impact of dataset size. Experiments conducted on an ADNI dataset containing 340 subjects and 1198 MRI brain scans have resulted good performance (with the test accuracy of 98.74%, 100% AD detection rate and 2,4% false alarm). Comparisons with 7 existing state-of-the-art methods have provided strong support to the robustness of the proposed method.
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| 15. |
- Fu, Keren, et al.
(author)
-
Deepside: A general deep framework for salient object detection
- 2019
-
In: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 356, s. 69-82
-
Journal article (peer-reviewed)abstract
- Deep learning-based salient object detection techniques have shown impressive results compared to con- ventional saliency detection by handcrafted features. Integrating hierarchical features of Convolutional Neural Networks (CNN) to achieve fine-grained saliency detection is a current trend, and various deep architectures are proposed by researchers, including “skip-layer” architecture, “top-down” architecture, “short-connection” architecture and so on. While these architectures have achieved progressive improve- ment on detection accuracy, it is still unclear about the underlying distinctions and connections between these schemes. In this paper, we review and draw underlying connections between these architectures, and show that they actually could be unified into a general framework, which simply just has side struc- tures with different depths. Based on the idea of designing deeper side structures for better detection accuracy, we propose a unified framework called Deepside that can be deeply supervised to incorporate hierarchical CNN features. Additionally, to fuse multiple side outputs from the network, we propose a novel fusion technique based on segmentation-based pooling, which severs as a built-in component in the CNN architecture and guarantees more accurate boundary details of detected salient objects. The effectiveness of the proposed Deepside scheme against state-of-the-art models is validated on 8 benchmark datasets.
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| 16. |
- Fu, Keren, 1988, et al.
(author)
-
Detection and Recognition of Traffic Signs from Videos using Saliency-Enhanced Features
- 2015
-
In: Nationell konferens i transportforskning, Oct. 21-22, 2015, Karstans universitet, Sweden.. ; , s. 2-
-
Conference paper (peer-reviewed)abstract
- Traffic sign recognition, including sign detection and classification, is an essential part in advanced driver assistance systems and autonomous vehicles. Traffic sign recognition (TSR), that exploits image analysis and computer vision techniques, has drawn increasing interest lately due to recently renewed efforts in vehicle safety and autonomous driving. Applications, among many others, include advanced driver assistance systems, sign inventory, intelligent autonomous driving.We propose efficient methods for detection and classification of traffic signs from automatically cropped street view images. The main novelties in the paper include:• An approach for automatic cropping of street view images from public available websites; The method detects and crops candidate traffic sign regions (bounding boxes) along the roads, from a specified route (i.e., the beginning and end points of the road), instead of conventionally using existing datasets;• An approach for generating saliency-enhanced features for the classifier. A novel method for obtaining the saliency-enhanced regions is proposed. It is based on a propagation process on enhancing sign part that attracts visual attention. Consequently, this leads to salient feature extraction. This approach overcomes the short coming in the conventional methods where features are extracted from the entire region of a detected bounding box which usually contains other clutter (or background).• A coarse-to-fine classification method that first classifies among different sign categories (e.g. categoryof forbidden, warning signs), followed by fine-classification of traffic signs within each category.The proposed methods have been tested on 2 categories of Chinese traffic signs, each containing many different signs.
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| 17. |
- Fu, Keren, 1988, et al.
(author)
-
Geodesic Distance Transform-based Salient Region Segmentation for Automatic Traffic Sign Recognition
- 2016
-
In: Proceedings - 2016 IEEE Intelligent Vehicles Symposium, IV 2016, Gotenburg, Sweden, 19-22 June 2016. - 9781509018215 ; 2016-August, s. 948-953
-
Conference paper (peer-reviewed)abstract
- Visual-based traffic sign recognition (TSR) requiresfirst detecting and then classifying signs from capturedimages. In such a cascade system, classification accuracy is often affected by the detection results. This paper proposes a method for extracting a salient region of traffic sign within a detection window for more accurate sign representation and feature extraction, hence enhancing the performance of classification. In the proposed method, a superpixel-based distance map is firstly generated by applying a signed geodesic distance transform from a set of selected foreground and background seeds. An effective method for obtaining a final segmentation from the distancemap is then proposed by incorporating the shape constraints of signs. Using these two steps, our method is able to automatically extract salient sign regions of different shapes. The proposed method is tested and validated in a complete TSR system. Test results show that the proposed method has led to a high classification accuracy (97.11%) on a large dataset containing street images. Comparing to the same TSR system without using saliency-segmented regions, the proposed method has yielded a marked performance improvement (about 12.84%). Future work will be on extending to more traffic sign categories and comparing with other benchmark methods.
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| 18. |
- Fu, Keren, 1988, et al.
(author)
-
Learning full-range affinity for diffusion-based saliency detection
- 2016
-
In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479999880 ; 2016-May, s. 1926-1930
-
Conference paper (peer-reviewed)abstract
- In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperforms 7 recent state-of-the-art saliency models on 3 benchmark datasets.
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| 19. |
- Fu, Keren, 1988, et al.
(author)
-
Normalized Cut-based Saliency Detection by Adaptive Multi-Level Region Merging
- 2015
-
In: IEEE Transactions on Image Processing. - 1941-0042 .- 1057-7149. ; 24:12, s. 5671-5683
-
Journal article (peer-reviewed)abstract
- Existing salient object detection models favor over-segmented regions upon which saliency is computed. Such local regions are less effective on representing object holistically and degrade emphasis of entire salient objects. As a result, existing methods often fail to highlight an entire object in complex background. Towards better grouping of objects and background, in this paper we consider graph cut, more specifically the Normalized graph cut (Ncut) for saliency detection. Since the Ncut partitions a graph in a normalized energy minimization fashion, resulting eigenvectors of the Ncut contain good cluster information that may group visual contents. Motivated by this, we directly induce saliency maps via eigenvectors of the Ncut, contributing to accurate saliency estimation of visual clusters. We implement the Ncut on a graph derived from a moderate number of superpixels. This graph captures both intrinsic color and edge information of image data. Starting from the superpixels, an adaptive multi-level region merging scheme is employed to seek such cluster information from Ncut eigenvectors. With developed saliency measures for each merged region, encouraging performance is obtained after across-level integration. Experiments by comparing with 13 existing methods on four benchmark datasets including MSRA-1000, SOD, SED and CSSD show the proposed method, Ncut saliency (NCS), results in uniform object enhancement and achieves comparable/better performance to the state-of-the-art methods.
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| 20. |
- Fu, Keren, 1988, et al.
(author)
-
Recognition of Chinese Traffic Signs from Street Views
- 2015
-
Reports (other academic/artistic)abstract
- This technical report describes the research work on automatic recognizing Chinese traffic signs from an implicit public resource, i.e. street views. First, we give a comprehensive survey on Chinese traffic signs and introduce our approaches for collecting street view images that can be used for experimental purposes. Then, we introduce our coarse-to-fine recognition framework consisting of sign detection, sign salient region segmentation, feature extraction (including simple text recognition from signs), and subsequent sign classification. We also propose to incrementally build a sign dataset in a semi-automatic way, aiming at reducing manual effort. Experiments on collected datasets for both sign detection and classification have validated that the proposed framework is feasible and capable of recognizing multiple categories of Chinese traffic signs in a single input image.
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| 21. |
- Fu, Keren, 1988, et al.
(author)
-
Refinet: A Deep Segmentation Assisted Refinement Network for Salient Object Detection
- 2019
-
In: IEEE Transactions on Multimedia. - 1520-9210. ; 21:2, s. 457-469
-
Journal article (peer-reviewed)abstract
- Deep convolutional neural networks (CNNs) recently have been successfully applied to saliency detection with improved performance on locating salient objects, as comparing to conventional saliency detection by handcrafted features. Unfortunately, due to repeated sub-sampling operations inside CNNs such as pooling and convolution, many CNN-based saliency models fail to maintain fine-grained spatial details and boundary structures of objects. To remedy this issue, this paper proposes a novel end-to-end deep learning-based refinement model named Refinet, which is based on fully convolutional network augmented with segmentation hypotheses. Intermediate saliency maps which are edge-aware are computed from segmentation-based pooling and then cancatenating two streams into a fully convolutional network for effective fusion and refinement, leading to more precise object details and boundaries. In addition, the resolution of feature maps in the proposed Refinet is carefully designed to guarantee sufficient boundary clarity of the refined saliency output. Compared to widely employed dense conditional random field (CRF), Refinet is able to enhance coarse saliency maps generated by existing models with more accurate spatial details, and its effectiveness is demonstrated by experimental results on 7 benchmark datasets.
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| 22. |
- Fu, Keren, 1988, et al.
(author)
-
Robust manifold-preserving diffusion-based saliency detection by adaptive weight construction
- 2016
-
In: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 175:Part A, s. 336-347
-
Journal article (peer-reviewed)abstract
- Graph-based diffusion techniques have drawn much interest lately for salient object detection. The diffusion performance is heavily dependent on the edge weights in graph representing the similarity between nodes, and are usually set through manually tuning. To improve the diffusion performance, this paper proposes a robust diffusion scheme, referred to as manifold-preserving diffusion (MPD), that is built jointly on two assumptions for preserving the manifold used in saliency detection. The smoothness assumption reflects the conditional random field (CRF) property and the related penalty term enforces similar saliency on similar graph neighbors. The penalty term related to the local reconstruction assumption enforces a local linear mapping from the feature space to saliency values. Graph edge weights in the above two penalties in the proposed MPD method are determined adaptively by minimizing local reconstruction errors in feature space. This enables a better adaption of diffusion on different images. The final diffusion process is then formulated as a regularized optimization problem, taking into account of initial seeds, manifold smoothness and local reconstruction. Consequently, when applied to saliency diffusion, MPD provides a higher performance upper bound than some existing diffusion methods such as manifold ranking. By utilizing MPD, we further introduce a two-stage saliency detection scheme, referred to as manifold-preserving diffusion-based saliency (MPDS), where boundary prior, Harris convex hull, and foci convex hull are employed for deriving initial seeds and a coarse map for MPD. Experiments were conducted on five benchmark datasets and compared with eight existing methods. Our results show that the proposed method is robust in terms of consistently achieving the highest weighted F-measure and lowest mean absolute error, meanwhile maintaining comparable precision–recall curves. Salient objects in different background can be uniformly highlighted in the output final saliency maps.
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| 23. |
- Fu, Keren, 1988, et al.
(author)
-
Saliency Detection by Fully Learning A Continuous Conditional Random Field
- 2017
-
In: IEEE Transactions on Multimedia. - 1520-9210. ; 19:7, s. 1531-1544
-
Journal article (peer-reviewed)abstract
- Salient object detection is aimed at detecting and segmenting objects that human eyes are most focused on whenviewing a scene. Recently, conditional random field (CRF) isdrawn renewed interest, and is exploited in this field. However, when utilizing a CRF with unary and pairwise potentials having essential parameters, most existing methods only employ manually designed parameters, or learn parameters partly for the unary potentials. Observing that the saliency estimation is a continuous labeling issue, this paper proposes a novel data driven scheme based on a special CRF framework, the so-called continuous CRF (C-CRF), where parameters for both unary and pairwise potentials are jointly learned. The proposed C-CRF learning provides an optimal way to integrate various unary saliency features with pairwise cues to discover salient objects. To the best of our knowledge, the proposed scheme is the first to completely learn a C-CRF for saliency detection. In addition, we propose a novel formulation of pairwise potentials that enables learning weights for different spatial ranges on a superpixel graph. The proposed C-CRF learning-based saliency model is tested on 6 benchmark datasets and compared with 11 existing methods. Our results and comparisons have provided further support to the proposed method in terms of precision-recall and F-measure. Furthermore, incorporating existing saliency models with pairwise cues through the C-CRF is shown to provide marked boosting performance over individual models.
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| 24. |
- Fu, Keren, 1988, et al.
(author)
-
SALIENT OBJECT DETECTION USING NORMALIZED CUT AND GEODESICS
- 2015
-
In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479983391 ; 2015-December, s. 1100-1104
-
Conference paper (peer-reviewed)abstract
- Normalized graph cut (Ncut) is conventionally used for partitioning a graph based on energy minimization, and is lately used for salient object detection. Observing that Ncut generates eigenvectors containing cluster information, we propose to incorporate eigenvectors of Ncut with the geodesic saliency detection model for obtaining enhanced salient object detection. In addition, appearance cue and intervening contour cue are jointly exploited for computing the graph affinity. The proposed method has been tested and evaluated on four benchmark datasets, and compared with 12 existing methods. Our results have provided strong support to the robustness of the proposed method.
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| 25. |
- Fu, Keren, 1988, et al.
(author)
-
Spectral salient object detection
- 2018
-
In: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 275, s. 788-803
-
Journal article (peer-reviewed)abstract
- Many salient object detection methods first apply pre-segmentation on image to obtain over-segmented regions to facilitate subsequent saliency computation. However, these pre-segmentation methods often ignore the holistic issue of objects and could degrade object detection performance. This paper proposes a novel method, spectral salient object detection, that aims at maintaining objects holistically during pre-segmentation in order to provide more reliable feature extraction from a complete object region and to facilitate object-level saliency estimation. In the proposed method, a hierarchical spectral partition method based on the normalized graph cut (Ncut) is proposed for image segmentation phase in saliency detection, where a superpixel graph that captures the intrinsic color and edge information of an image is constructed and then hierarchically partitioned. In each hierarchy level, a region constituted by superpixels is evaluated by criteria based on figure-ground principles and statistical prior to obtain a regional saliency score. The coarse salient region is obtained by integrating multiple saliency maps from successive hierarchies. The final saliency map is derived by minimizing the graph-based semi-supervised learning energy function on the synthetic coarse saliency map. Despite the simple intuition of maintaining object holism, experimental results on 5 benchmark datasets including ASD, ECSSD, MSRA, PASCAL-S, DUT-OMRON demonstrate encouraging performance of the proposed method, along with the comparisons to 13 state-of-the-art methods. The proposed method is shown to be effective on emphasizing large/medium-sized salient objects uniformly due to the employment of Ncut. Besides, we conduct thorough analysis and evaluation on parameters and individual modules.
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| 26. |
- Fu, Keren, 1988, et al.
(author)
-
Traffic Sign Recognition using Salient Region Features: A Novel Learning-based Coarse-to-Fine Scheme
- 2015
-
In: IEEE Intelligent Vehicles Symposium, June 28-July 1, 2015, Seoul, Korea. - 9781467372664 ; 2015-August, s. 443-448
-
Conference paper (peer-reviewed)abstract
- Traffic sign recognition, including sign detection and classification, is essential for advanced driver assistancesystems and autonomous vehicles. This paper introduces a novel machine learning-based sign recognition scheme. In the proposed scheme, detection and classification are realized through learning in a coarse-to-fine manner. Based on the observation that signs in the same category share some common attributes in appearance, the proposed scheme first distinguishes each individual sign category from the background in the coarse learning stage (i.e. sign detection) followed by distinguishing different sign classes within each category in the fine learning stage (i.e. sign classification). Both stages are realized throughmachine learning techniques. A complete recognition scheme is developed that is effective for simultaneously recognizing multiple categories of traffic signs. In addition, a novel saliency-based feature extraction method is proposed for sign classification. The method segments salient sign regions by leveraging the geodesic energy propagation. Compared with the conventional feature extraction, our method provides more reliable feature extraction from salient sign regions. The proposed scheme istested and validated on two categories of Chinese traffic signs from Tencent street view. Evaluations on the test dataset show reasonably good performance, with an average of 97.5% true positive and 0.3% false positive on two categories of traffic signs.
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| 27. |
- Gams, Matjaz, et al.
(author)
-
Artificial intelligence and ambient intelligence
- 2019
-
In: Journal of Ambient Intelligence and Smart Environments. - 1876-1364. ; 11:1, s. 71-86
-
Journal article (peer-reviewed)abstract
- Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore’s law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper.
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| 28. |
- Ge, Chenjie, 1991, et al.
(author)
-
3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images
- 2018
-
In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479970612 ; , s. 141-145
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues of grading brain tumor, glioma, from Magnetic Resonance Images (MRIs). Although feature pyramid is shown to be useful to extract multi-scale features for object recognition, it is rarely explored in MRI images for glioma classification/grading. For glioma grading, existing deep learning methods often use convolutional neural networks (CNNs) to extract single-scale features without considering that the scales of brain tumor features vary depending on structure/shape, size, tissue smoothness, and locations. In this paper, we propose to incorporate the multi-scale feature learning into a deep convolutional network architecture, which extracts multi-scale semantic as well as fine features for glioma tumor grading. The main contributions of the paper are: (a) propose a novel 3D multi-scale convolutional network architecture for the dedicated task of glioma grading; (b) propose a novel feature fusion scheme that further refines multi-scale features generated from multi-scale convolutional layers; (c) propose a saliency-aware strategy to enhance tumor regions of MRIs. Experiments were conducted on an open dataset for classifying high/low grade gliomas. Performance on the test set using the proposed scheme has shown good results (with accuracy of 89.47%).
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| 29. |
- Ge, Chenjie, 1991, et al.
(author)
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Co-Saliency-Enhanced Deep Recurrent Convolutional Networks for Human Fall Detection in E-Healthcare
- 2018
-
In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. ; , s. 1572-1575
-
Conference paper (peer-reviewed)abstract
- This paper addresses the issue of fall detection from videos for e-healthcare and assisted-living. Instead of using conventional hand-crafted features from videos, we propose a fall detection scheme based on co-saliency-enhanced recurrent convolutional network (RCN) architecture for fall detection from videos. In the proposed scheme, a deep learning method RCN is realized by a set of Convolutional Neural Networks (CNNs) in segment-levels followed by a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), to handle the time-dependent video frames. The co-saliency-based method enhances salient human activity regions hence further improves the deep learning performance. The main contributions of the paper include: (a) propose a recurrent convolutional network (RCN) architecture that is dedicated to the tasks of human fall detection in videos; (b) integrate a co-saliency enhancement to the deep learning scheme for further improving the deep learning performance; (c) extensive empirical tests for performance analysis and evaluation under different network settings and data partitioning. Experiments using the proposed scheme were conducted on an open dataset containing multicamera videos from different view angles, results have shown very good performance (test accuracy 98.96%). Comparisons with two existing methods have provided further support to the proposed scheme.
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| 30. |
- Ge, Chenjie, 1991, et al.
(author)
-
Cross-Modality Augmentation of Brain Mr Images Using a Novel Pairwise Generative Adversarial Network for Enhanced Glioma Classification
- 2019
-
In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880.
-
Conference paper (peer-reviewed)abstract
- © 2019 IEEE. Brain Magnetic Resonance Images (MRIs) are commonly used for tumor diagnosis. Machine learning for brain tumor characterization often uses MRIs from many modalities (e.g., T1-MRI, Enhanced-T1-MRI, T2-MRI and FLAIR). This paper tackles two issues that may impact brain tumor characterization performance from deep learning: insufficiently large training dataset, and incomplete collection of MRIs from different modalities. We propose a novel pairwise generative adversarial network (GAN) architecture for generating synthetic brain MRIs in missing modalities by using existing MRIs in other modalities. By improving the training dataset, we aim to mitigate the overfitting and improve the deep learning performance. Main contributions of the paper include: (a) propose a pairwise generative adversarial network (GAN) for brain image augmentation via cross-modality image generation; (b) propose a training strategy to enhance the glioma classification performance, where GAN-augmented images are used for pre-training, followed by refined-training using real brain MRIs; (c) demonstrate the proposed method through tests and comparisons of glioma classifiers that are trained from mixing real and GAN synthetic data, as well as from real data only. Experiments were conducted on an open TCGA dataset, containing 167 subjects for classifying IDH genotypes (mutation or wild-type). Test results from two experimental settings have both provided supports to the proposed method, where glioma classification performance has consistently improved by using mixed real and augmented data (test accuracy 81.03%, with 2.57% improvement).
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| 31. |
- Ge, Chenjie, 1991, et al.
(author)
-
Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks
- 2018
-
In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. ; , s. 5894-5897
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues of brain tumor, glioma, grading from multi-sensor images. Different types of scanners (or sensors) like enhanced T1-MRI, T2-MRI and FLAIR, show different contrast and are sensitive to different brain tissues and fluid regions. Most existing works use 3D brain images from single sensor. In this paper, we propose a novel multistream deep Convolutional Neural Network (CNN) architecture that extracts and fuses the features from multiple sensors for glioma tumor grading/subcategory grading. The main contributions of the paper are: (a) propose a novel multistream deep CNN architecture for glioma grading; (b) apply sensor fusion from T1-MRI, T2-MRI and/or FLAIR for enhancing performance through feature aggregation; (c) mitigate overfitting by using 2D brain image slices in combination with 2D image augmentation. Two datasets were used for our experiments, one for classifying low/high grade gliomas, another for classifying glioma with/without 1p19q codeletion. Experiments using the proposed scheme have shown good results (with test accuracy of 90.87% for former case, and 89.39 % for the latter case). Comparisons with several existing methods have provided further support to the proposed scheme.
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| 32. |
- Ge, Chenjie, 1991, et al.
(author)
-
Human Fall Detection using Co-Saliency-Enhanced Deep Recurrent Convolutional Neural Networks
- 2019
-
In: Internationa Research Journal of Engineering and Technology (IRJET). - 2395-0056. ; 6:9, s. 993-1000
-
Journal article (peer-reviewed)abstract
- This paper addresses issues of fall detection from videos for e-healthcare and assisted-living. Instead of using hand-crafted features from videos, we exploit a dedicated recurrent convolutional network (RCN) architecture for fall detection in combination with co-saliency enhancement. In the proposed scheme, the recurrent neural network (RNN) is realized by Long Short-Term Memory (LSTM) connecting to a set of Convolutional Neural Networks (CNNs), where each video is modelled as an ordered sequence, containing several frames. In such a way, the sequential information in video is preserved. To further enhance the performance, we propose to employ co-saliency-enhanced video frames as the inputs of RCN, where salient human activity regions are enhanced. Experimental results have shown that the proposed scheme is effective. Further, our results have shown very good test performance (accuracy 98.12%), and employing the co-saliency-enhanced RCN has led to the improvement in performance (0.70% on test) as comparing to that without co-saliency. Comparisons with two existing methods have provided further support to effectiveness of the proposed scheme.
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| 33. |
- Ge, Chenjie, 1991, et al.
(author)
-
Human fall detection using segment-level CNN features and sparse dictionary learning
- 2017
-
In: IEEE International Workshop on Machine Learning for Signal Processing, MLSP. - 2161-0371 .- 2161-0363. ; 2017-September, s. 6-
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues in human fall detection from videos. Unlike using handcrafted features in the conventional machine learning, we extract features from Convolutional Neural Networks (CNNs) for human fall detection. Similar to many existing work using two stream inputs, we use a spatial CNN stream with raw image difference and a temporal CNN stream with optical flow as the inputs of CNN. Different from conventional two stream action recognition work, we exploit sparse representation with residual-based pooling on the CNN extracted features, for obtaining more discriminative feature codes. For characterizing the sequential information in video activity, we use the code vector from long-range dynamic feature representation by concatenating codes in segment-levels as the input to a SVM classifier. Experiments have been conducted on two public video databases for fall detection. Comparisons with six existing methods show the effectiveness of the proposed method.
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| 34. |
- Ge, Chenjie, 1991, et al.
(author)
-
Multi-Stream Multi-Scale Deep Convolutional Networks for Alzheimer's Disease Detection using MR Images
- 2019
-
In: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 350, s. 60-69
-
Journal article (peer-reviewed)abstract
- This paper addresses the issue of Alzheimer's disease (AD) detection from Magnetic Resonance Images (MRIs). Existing AD detection methods rely on global feature learning from the whole brain scans, while depending on the tissue types, AD related features in dierent tissue regions, e.g. grey matter (GM), white matter (WM), and cerebrospinal uid (CSF), show different characteristics. In this paper, we propose a deep learning method for multi-scale feature learning based on segmented tissue areas. A novel deep 3D multi-scale convolutional network scheme is proposed to generate multi-resolution features for AD detection. The proposed scheme employs several parallel 3D multi-scale convolutional networks, each applying to individual tissue regions (GM, WM and CSF) followed by feature fusions. The proposed fusion is applied in two separate levels: the rst level fusion is applied on different scales within the same tissue region, and the second level is on dierent tissue regions. To further reduce the dimensions of features and mitigate overtting, a feature boosting and dimension reduction method, XGBoost, is utilized before the classication. The proposed deep learning scheme has been tested on a moderate open dataset of ADNI (1198 scans from 337 subjects), with excellent test performance on randomly partitioned datasets (best 99.67%, average 98.29%), and good test performance on subject-separated partitioned datasets (best 94.74%, average 89.51%). Comparisons with state-of-the-art methods are also included.
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| 35. |
- Ge, Chenjie, 1991, et al.
(author)
-
Multiscale Deep Convolutional Networks for Characterization and Detection of Alzheimer's Disease using MR Images
- 2019
-
In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. ; 2019-September, s. 789-793
-
Conference paper (peer-reviewed)abstract
- This paper addresses the issues of Alzheimer's disease (AD) characterization and detection from Magnetic Resonance Images (MRIs). Many existing AD detection methods use single-scale feature learning from brain scans. In this paper, we propose a multiscale deep learning architecture for learning AD features. The main contributions of the paper include: (a) propose a novel 3D multiscale CNN architecture for the dedicated task of AD detection; (b) propose a feature fusion and enhancement strategy for multiscale features; (c) empirical study on the impact of several settings, including two dataset partitioning approaches, and the use of multiscale and feature enhancement. Experiments were conducted on an open ADNI dataset (1198 brain scans from 337 subjects), test results have shown the effectiveness of the proposed method with test accuracy of 93.53%, 87.24% (best, average) on subject separated dataset, and 99.44%, 98.80% (best, average) on random brain scan-partitioned dataset. Comparison with eight existing methods has provided further support to the proposed method.
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| 36. |
- Gu, Irene Yu-Hua, 1953, et al.
(author)
-
3D Limb Movement Tracking and Analysis for Neurological Dysfunctions of Neonates Using Multi-Camera Videos
- 2016
-
In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781457702204 ; 2016-October, s. 2395-2398
-
Conference paper (peer-reviewed)abstract
- Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection of limb movement anomalies associated with such neurological dysfunctions in infants is the first step towards early treatment for improving infant development. This paper addresses the issue of detecting and quantifying limb movement anomalies in infants through non-invasive 3D image analysis methods using videos from multiple camera views. We propose a novel scheme for tracking 3D time trajectories of markers on infant’s limbs by video analysis techniques. The proposed scheme employ videos captured from three camera views. This enables us to detect a set of enhanced 3D markers through cross-view matching and to effectively handle marker self-occlusions by other body parts. We track a set of 3D trajectories of limb movements by a set of particle filters in parallel, enabling more robust 3D tracking of markers, and use the 3D model errors for quantifying abrupt limb movements. The proposed work makes a significant advancement to the previous work in [1] through employing tracking in 3D space, and hence overcome several main barriers that hinder real applications by using single camera-based techniques. To the best of our knowledge, applying such a multi-view video analysis approach for assessing neurological dysfunctions of infants through 3D time trajectories of markers on limbs is novel, and could lead to computer-aided tools for diagnosis of dysfunctions where early treatment may improve infant development. Experiments were conducted on multi-view neonate videos recorded in a clinical setting and results have provided further support to the proposed method.
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| 37. |
- Gu, Irene Yu-Hua, 1953, et al.
(author)
-
Privacy-Preserving Fall Detection in Healthcare using Shape and Motion Features from Low-Resolution RGB-D Videos
- 2016
-
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319415017 ; 9730, s. 490-499
-
Conference paper (peer-reviewed)abstract
- This paper addresses the issue on fall detection in healthcare using RGB-D videos. Privacy is often a major concern in video-based detection and analysis methods. We propose a video-based fall detection scheme with privacy preserving awareness. First, a set of features is defined and extracted, including local shape and shape dynamic features from object contours in depth video frames, and global appearance and motion features from HOG and HOGOF in RGB video frames. A sequence of time-dependent features is then formed by a sliding window averaging of features along the temporal direction, and use this as the input of a SVM classifier for fall detection. Separate tests were conductedon a large dataset for examining the fall detection performance with privacy-preserving awareness. These include testing the fall detection scheme that solely uses depth videos, solely uses RGB videos in different resolution, as well as the influence of individual features and feature fusion to the detection performance. Our test results show that both the dynamic shape features from depth videos and motion (HOGOF) features from low- resolution RGB videos may preserve the privacy meanwhileyield good performance (91.88% and 97.5% detection, with false alarm ≤ 1.25 %). Further, our results show that the proposed scheme is able to discriminate highly confused classes of activities (falling versus lying down) with excellent performance. Our study indicates that methods based on depth or low-resolution RGB videos may still provide effective technologies for the healthcare, without impact personnel privacy.
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| 38. |
- Kumar, Durga Priya, 1990, et al.
(author)
-
Fall detection in RGB-D videos by combining shape and motion features
- 2016
-
In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479999880 ; 2016-May, s. 1337-1341
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues in fall detection from RGB-D videos. The study focuses on measuring the dynamics of shape and motion of the target person, based on the observation that a fall usually causes drastic large shape deformation and physical movement. The main novelties include: (a) forming contours of target persons in depth images based on morphological skeleton; (b) extracting local dynamic shape and motion features from target contours; (c) encoding global shape and motion in HOG and HOGOF features from RGB images; (d) combining various shape and motion features for enhanced fall detection. Experiments have been conducted on an RGB-D video dataset for fall detection. Results show the effectiveness of the proposed method.
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| 39. |
- Liu, Fanghui, et al.
(author)
-
Robust visual tracking via inverse nonnegative matrix factorization
- 2016
-
In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479999880 ; 2016-May, s. 1491-1495
-
Conference paper (peer-reviewed)abstract
- The establishment of robust target appearance model over time is an overriding concern in visual tracking. In this paper, we propose an inverse nonnegative matrix factorization (NMF) method for robust appearance modeling. Rather than using a linear combination of nonnegative basis vectors for each target image patch in conventional NMF, the proposed method is a reverse thought to conventional NMF tracker. It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors. Inverse NMF is used as a feature encoder, where the resulting coefficient vectors are fed into a SVM classifier for separating the target from the background. The proposed method is tested on several videos and compared with seven state-of-the-art methods. Our results have provided further support to the effectiveness and robustness of the proposed method.
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| 40. |
- Liu, Fanghui, et al.
(author)
-
Visual Tracking via Nonnegative Regularization Multiple Locality Coding
- 2015
-
In: Proceedings of the IEEE International Conference on Computer Vision. - 1550-5499. - 9780769557205 ; 2015-February, s. 912-920
-
Conference paper (peer-reviewed)abstract
- This paper presents a novel object tracking method based on approximated Locality-constrained Linear Coding (LLC). Rather than using a non-negativity constraint on encoding coefficients to guarantee these elements nonnegative, in this paper, the non-negativity constraint is substituted for a conventional ℓ2 norm regularization term in approximated LLC to obtain the similar nonnegative effect. And we provide a detailed and adequate explanation in theoretical analysis to clarify the rationality of this replacement. Instead of specifying fixed K nearest neighbors to construct the local dictionary, a series of different dictionaries with pre-defined numbers of nearest neighbors are selected. Weights of these various dictionaries are also learned from approximated LLC in the similar framework. In order to alleviate tracking drifts, we propose a simple and efficient occlusion detection method. The occlusion detection criterion mainly depends on whether negative templates are selected to represent the severe occluded target. Both qualitative and quantitative evaluations on several challenging sequences show that the proposed tracking algorithm achieves favorable performance compared with other state-of-the-art methods.
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| 41. |
- Morena-Garcia, Isabel, et al.
(author)
-
Tests and Analysis of a novel Segmentation method using Measurement Data
- 2015
-
In: CIRED 23rd Int’l Conf. on Electricity Distribution, 15-18 June, 2015, Lyon, France.. ; , s. 5-
-
Conference paper (peer-reviewed)abstract
- Fault detection in power systems and its diagnosis are highly relevant issues within a power quality scope. Detailed analysis of disturbance recordings, like voltage dips, requires accurate segmentation methods. A joint causal and anti-causal (CaC) segmentation method has been introduced but only been tested with synthetic signals. In this paper, its performance has been analysed with a set of real measurement signals.
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| 42. |
- Moreno-Garcia, I.M., et al.
(author)
-
Novel segmentation technique for measured three-phase voltage dips
- 2015
-
In: Energies. - : MDPI AG. - 1996-1073 .- 1996-1073. ; 8:8, s. 8319-8338
-
Journal article (peer-reviewed)abstract
- This paper focuses on issues arising from the need to automatically analyze disturbances in the future (smart) grid. Accurate time allocation of events and the sequences of events is an important part of such an analysis. The performance of a joint causal and anti-causal (CaC) segmentation method has been analyzed with a set of real measurement signals, using an alternative detection technique based on a cumulative sum (CUSUM) algorithm. The results show that the location in time of underlying transitions in the power system can be more accurately estimated by combining CaC segmentation methods.
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| 43. |
- Moreno, Isabel, et al.
(author)
-
Causal and anti-causal segmentation of voltage dips in power distribution networks
- 2016
-
In: IEEE Latin America Transactions. - 1548-0992. ; 14:7, s. 3080-3086
-
Journal article (peer-reviewed)abstract
- This paper presents the performance of a joint causal and anti-causal (CaC) segmentation method for automatic location of nonstationary parts of power quality (PQ) events. Accurate time allocation of events and sequences of events is an important study to automatically analyze disturbances in the future (smart) grid. The new method developed is based on the cumulative sum (CUSUM) algorithm and is applied to a wide set of power quality events to analyze its performance. The main advantage of CaC segmentation is that the location in time of underlying transitions in the power system is accurately estimated.
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| 44. |
- Rönnberg, Sarah, et al.
(author)
-
On waveform distortion in the frequency range of 2 kHz–150 kHz—Review and research challenges
- 2017
-
In: Electric Power Systems Research. - : Elsevier BV. - 0378-7796 .- 1873-2046. ; 150, s. 1-10
-
Research review (peer-reviewed)abstract
- The frequency range between 2 and 150 kHz has recently gained significant attention, triggered by standardization needs and increased emission in this wide frequency range. This paper gives an overview of the state-of-the-art concerning these so-called supraharmonics, and noticeably indicates the research challenges associated with waveform distortion in this frequency range, with emphasis on the following aspects: emission; propagation; interference; measurements; standardization; modelling and simulation.
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| 45. |
- Xu, Long, et al.
(author)
-
Video-based Tracking and Quantified Assessment of Spontaneous Limb Movements in Neonates
- 2015
-
In: 17th IEEE Int'l conf. on E-Health, Networking, Application & Services (HealthCom'15), 2015. - 9781467383257 ; , s. 517-522
-
Conference paper (peer-reviewed)abstract
- Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limbmovements. Detection and quantification of these movements in infants from videos are hence desirable for providing useful information to clinicians. This could lead to computer-aided diagnosis of dysfunctions where early treatment may improve infant development. In this paper, we propose a scheme for detecting and quantifying qualitative aspects of limb movement through multiple tracking and state space motion modeling on videos. The main novelties of the paper include: (a) An enhanced detection method for effectively detection small weak marker points from video; (b) Bayesian estimation and nearest neighbor searching for selecting new observation in individual tracker and for tracking marker trajectories on limbs; (c) A criterion foranomaly detection based on the frequency and duration of abrupt changes in limb movement, using window averaged prominent residual powers. The proposed method has been tested on videos of neonates, results show that the proposed method is promising for tracking and quantifying the movement of neonate limbs for helping medical diagnostics.
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| 46. |
- Yun, Yixiao, 1987, et al.
(author)
-
Exploiting Riemannian Manifolds for Daily Activity Classification in Video Towards Health Care
- 2016
-
In: IEEE International Conference on E-health Networking, Application & Services (HealthCom 2016), Munich, Germany, Sept. 14-17, 2016.. - 9781509033706 ; , s. 363-368
-
Conference paper (peer-reviewed)abstract
- This paper addresses the problem of classifying activities of daily living in video. The proposed method uses a tree structure of two layers, where in each node of the tree there resides a Riemannian manifold that corresponds to different part-based covariance features. In the first layer, activities are classified according to the dynamics of upper body parts. In the second layer, activities are further classified according to the appearance of local image patches at hands in key frames, where the interacting objects are likely to be attached. The novelties of this paper include: (i) characterizing the motion of upper body parts by a covariance matrix of distances between each pair of key points and the orientations of lines that connect them; (ii) describing human-object interaction by the appearance of local regions around hands in key frames that are selected based on the proximity of hands to other key points; (iii) formulating a pairwise geodesics-based kernel for activity classification on Riemannian manifolds under the log-Euclidean metric. Experiments were conducted on a video dataset containing a total number of 426 video events (activities) from 4 classes. The proposed method is shown to be effective by achieving high classification accuracy (93.79% on average) and small false alarms (1.99% on average) overall, as well as for each individual class.
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| 47. |
- Yun, Yixiao, 1987, et al.
(author)
-
Fall Detection in RGB-D Videos for Elderly Care
- 2015
-
In: 17th IEEE Int'l conf. on E-Health, Networking, Application & Services (HealthCom'15), 2015. - 9781467383257 ; , s. 6-
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues in fall detection from videos. Since it has been a broadly accepted intuition that a falling person usually undergoes large physical movement anddisplacement in a short time interval, the study is thus focused on measuring the intensity and temporal variation of pose change and body motion. The main novelties of this paper include: (a) characterizing pose/motion dynamics based on centroid velocity, head-to-centroid distance, histogram of oriented gradients and optical flow; (b) extracting compact features based on the mean and variance of pose/motion dynamics; (c) detecting human by combining depth information and background mixture models. Experiments have been conducted on an RGB-D video datasetfor fall detection. Tests and evaluations show the effectiveness of the proposed method.
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| 48. |
- Yun, Yixiao, 1987, et al.
(author)
-
Human Fall Detection in Videos by Fusing Statistical Features of Shape and Motion Dynamics on Riemannian Manifolds
- 2016
-
In: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 207, s. 726-734
-
Journal article (peer-reviewed)abstract
- This paper addresses issues in fall detection in videos. We propose a novel method to detect human falls from arbitrary view angles, through analyzing dynamic shape and motion of image regions of human bodies on Riemannian manifolds. The proposed method exploits time-dependent dynamic features on smooth manifolds based on the observation that human falls often involve drastically shape changes and abrupt motions as comparing with other activities. The main novelties of this paper include: (a) representing videos of human activities by dynamic shape points and motion points moving on two separate unit n-spheres, or, two simple Riemannian manifolds; (b) characterizing the dynamic shape and motion of each video activity by computing the velocity statistics on the two manifolds, based on geodesic distances; (c) combining the statistical features of dynamic shape and motion that are learned from their corresponding manifolds via mutual information. Experiments were conducted on three video datasets, containing 400 videos of 5 activities, 100 videos of 4 activities, and 768 videos of 3 activities, respectively, where videos were captured from cameras in different view angles. Our test results have shown high detection rate (average 99.38%) and low false alarm (average 1.84%). Comparisons with eight state-of-the-art methods have provided further support to the proposed method.
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| 49. |
- Yun, Yixiao, 1987, et al.
(author)
-
Human fall detection in videos via boosting and fusing statistical features of appearance, shape and motion dynamics on Riemannian manifolds with applications to assisted living
- 2016
-
In: Computer Vision and Image Understanding. - : Elsevier BV. - 1077-3142 .- 1090-235X. ; 148, s. 111-122
-
Journal article (peer-reviewed)abstract
- This paper addresses issues in fall detection from videos. It is commonly observed that a falling person undergoes large appearance change, shape deformation and physical displacement, thus the focus here is on the analysis of these dynamic features that vary drastically in camera views while a person falls onto the ground. A novel approach is proposed that performs such analysis on Riemannian manifolds, detecting falls from a single camera with arbitrary view angles. The main novelties of this paper include: (a) representing the dynamic appearance, shape and motion of a target person each being points moving on a different Riemannian manifold; (b) characterizing the dynamics of different features by computing velocity statistics of their corresponding manifold points, based on geodesic distances; (c) employing a feature weighting approach, where each statistical feature is weighted according to the mutual information; (d) fusing statistical features learned from different manifolds with a two-stage ensemble learning strategy under a boosting framework. Experiments have been conducted on two video datasets for fall detection. Tests, evaluations and comparisons with 6 state-of-the-art methods have provided support to the effectiveness of the proposed method.
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| 50. |
- Yun, Yixiao, 1987, et al.
(author)
-
Human Fall Detection via Shape Analysis on Riemannian Manifolds with Applications to Elderly Care
- 2015
-
In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479983391 ; 2015-December, s. 3280-3284
-
Conference paper (peer-reviewed)abstract
- This paper addresses issues in fall detection from videos. The focus is on the analysis of human shapes which deform drastically in camera views while a person falls onto the ground. A novel approach is proposed that performs fall detection from an arbitrary view angle, via shape analysis on a unified Riemannian manifold for different camera views. The main novelties of this paper include: (a) representing dynamic shapes as points moving on a unit n-sphere, one of the simplest Riemannian manifolds; (b) characterizing the deformation of shapes by computing velocity statistics of their corresponding manifold points, based on geodesic distances on the manifold. Experiments have been conducted on two publicly available video datasets for fall detection. Test, evaluations and comparisons with 6 existing methods show the effectiveness of our proposed method.
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