David Dagan Feng

Budapest University of Technology and Economics, Budapeŝto, Budapest, Hungary

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Publications (259)143.76 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: A minimum sparse reconstruction (MSR) based video summarization (VS) model is constructed.•An L0 norm based constraint is imposed to ensure real sparsity.•Two efficient and effective MSR based VS algorithms are proposed for off-line and on-line applications, respectively.•A scalable strategy is designed to provide flexibility for practical applications.
    Pattern Recognition 02/2015; 48(2). · 2.58 Impact Factor
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    ABSTRACT: Accurate approximation of noise in hyperspectral (HS) images plays an important role in better visualization and image processing. Conventional algorithms often hypothesize the noise type to be either purely additive or of a mixed noise type for the signal-dependent (SD) noise component and the signal-independent (SI) noise component in HS images. This can result in application-driven algorithm design and limited use in different noise types. Moreover, as the highly textured HS images have abundant edges and textures, existing algorithms may fail to produce accurate noise estimation. To address these challenges, we propose a noise estimation algorithm that can adaptively estimate both purely additive noise and mixed noise in HS images with various complexities. First, homogeneous areas are automatically detected using a new region-growing-based approach, in which the similarity of two pixels is calculated by a robust spectral metric. Then, the mixed noise variance of each homogeneous region is estimated based on multiple linear regression technology. Finally, intensities of the SD and SI noise are obtained with a modified scatter plot approach. We quantitatively evaluated our algorithm on the synthetic HS data. Compared with the benchmarking and state-of-the-art algorithms, the proposed algorithm is more accurate and robust when facing images with different complexities. Experimental results with real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images further demonstrated the superiority of our algorithm.
    Applied Optics 10/2014; 53(30). · 1.69 Impact Factor
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    ABSTRACT: Objective video quality assessment is of great importance in a variety of video processing applications. Most existing video quality metrics either focus primarily on capturing spatial artifacts in the video signal, or are designed to assess only grayscale video thereby ignoring important chrominance information. In this paper, on the basis of the top-down visual analysis of cognitive understanding and video features, we propose and develop a novel full-reference perceptual video assessment technique that accepts visual information inputs in the form of a quaternion consisting of contour, color and temporal information. Because of the more important role of chrominance information in the “border-to-surface” mechanism at early stages of cognitive visual processing, our new metric takes into account the chrominance information rather than the luminance information utilized in conventional video quality assessment. Our perceptual quaternion model employs singular value decomposition (SVD) and utilizes the human visual psychological features for SVD block weighting to better reflect perceptual focus and interest. Our major contributions include: a new perceptual quaternion that takes chrominance as one spatial feature, and temporal information to model motion or changes across adjacent frames; a three-level video quality measure to reflect visual psychology; and the two weighting methods based on entropy and frame correlation. Our experimental validation on the video quality experts’ group (VQEG) Phase I FR-TV test dataset demonstrated that our new assessment metric outperforms PSNR, SSIM, PVQM (P8) and has high correlation with perceived video quality.
    Multimedia Tools and Applications 10/2014; · 1.06 Impact Factor
  • S. Lu, Z. Wang, T. Mei, G. Guan, D.D. Feng
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    ABSTRACT: Video summarization helps users obtain quick comprehension of video content. Recently, some studies have utilized local features to represent each video frame and formulate video summarization as a coverage problem of local features. However, the importance of individual local features has not been exploited. In this paper, we propose a novel Bag-of-Importance (BoI) model for static video summarization by identifying the frames with important local features as keyframes, which is one of the first studies formulating video summarization at local feature level, instead of at global feature level. That is, by representing each frame with local features, a video is characterized with a bag of local features weighted with individual importance scores and the frames with more important local features are more representative, where the representativeness of each frame is the aggregation of the weighted importance of the local features contained in the frame. In addition, we propose to learn a transformation from a raw local feature to a more powerful sparse nonlinear representation for deriving the importance score of each local feature, rather than directly utilize the hand-crafted visual features like most of the existing approaches. Specifically, we first employ locality-constrained linear coding (LCC) to project each local feature into a sparse transformed space. LCC is able to take advantage of the manifold geometric structure of the high dimensional feature space and form the manifold of the low dimensional transformed space with the coordinates of a set of anchor points. Then we calculate the ${l_2}$ norm of each anchor point as the importance score of each local feature which is projected to the anchor point. Finally, the distribution of the importance scores of all the local features in a video is obtained as the BoI representation of the video. We further differentiate the importance of local feat- res with a spatial weighting template by taking the perceptual difference among spatial regions of a frame into account. As a result, our proposed video summarization approach is able to exploit both the inter-frame and intra-frame properties of feature representations and identify keyframes capturing both the dominant content and discriminative details within a video. Experimental results on three video datasets across various genres demonstrate that the proposed approach clearly outperforms several state-of-the-art methods.
    IEEE Transactions on Multimedia 10/2014; 16(6):1497-1509. · 1.78 Impact Factor
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    ABSTRACT: Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various types of features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using the local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, for disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could best distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to the global methods and other pattern analysis methods based on clinical expertise or test statistics. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights of underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.
    Computerized Medical Imaging and Graphics 09/2014; · 1.50 Impact Factor
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    ABSTRACT: Multi-modality positron emission tomography and computed tomography (PET-CT) imaging depicts biological and physiological functions (from PET) within a higher resolution anatomical reference frame (from CT). The need to efficiently assimilate the information from these co-aligned volumes simultaneously has resulted in 3D visualisation methods that depict e.g., slice of interest (SOI) from PET combined with direct volume rendering (DVR) of CT. However because DVR renders the whole volume, regions of interests (ROIs) such as tumours that are embedded within the volume may be occluded from view. Volume clipping is typically used to remove occluding structures by `cutting away' parts of the volume; this involves tedious trail-and-error tweaking of the clipping attempts until a satisfied visualisation is made, thus restricting its application. Hence, we propose a new automated opacity-driven volume clipping method for PET-CT using DVR-SOI visualisation. Our method dynamically calculates the volume clipping depth by considering the opacity information of the CT voxels in front of the PET SOI, thereby ensuring that only the relevant anatomical information from the CT is visualised while not impairing the visibility of the PET SOI. We outline the improvements of our method when compared to conventional 2D and traditional DVR-SOI visualisations.
    08/2014; 2014:6714-7.
  • Ying Li, Shi Liang, Bendu Bai, David Feng
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    ABSTRACT: This paper presents a unified framework for automatically detecting and tracking dim small targets in infrared (IR) image sequence under complex backgrounds. Firstly, the variance weighted information entropy (variance WIE) followed by a region growing technique is introduced to segment the candidate targets in a single-frame IR image after background suppression. Then the pipeline filter is used to verify the real targets. The position and the size of the detected target are then obtained to initialize the tracking algorithm. Secondly, we adopt an improved local binary pattern (LBP) scheme to represent the target texture feature and propose a joint gray-texture histogram method for a more distinctive and effective target representation. Finally, target tracking is accomplished by using the mean shift algorithm. Experimental results indicate that the proposed method can effectively detect the dim small targets under complex backgrounds and has better tracking performance compared with the gray histogram based tracking methods such as the mean shift and the particle filtering.
    Multimedia Tools and Applications 08/2014; · 1.06 Impact Factor
  • Lei Bi, Jinman Kim, David Dagan Feng, Michael Fulham
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    ABSTRACT: Fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is the preferred image modality for lymphoma diagnosis. Sites of disease generally appear as foci of increased FDG uptake. Thresholding methods are often applied to robustly separate these regions. However, its main limitation is that it also includes sites of FDG excretion and physiological FDG uptake regions, which we define as FEPU - sites of FEPU include the bladder, renal, papillae, ureters, brain, heart and brown fat. FEPU can make image interpretation problematic. The ability to identify and label FEPU sites and separate them from abnormal regions is an important process that could improve image interpretation. We propose a new method to automatically separate and label FEPU sites from the thresholded PET images. Our method is based on the selective use of features extracted from data types comprising of PET, CT and PET-CT. Our FEPU classification of 43 clinical lymphoma patient studies revealed higher accuracy when compared to non-selective image features.
    08/2014; 2014:1913-6.
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    ABSTRACT: With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance of the hierarchical spatial descriptor, as well as presents a new binary descriptor for the retrieval of lung nodule images. The descriptors were evaluated on the ELCAP public access database, exhibiting good performance overall.
    08/2014; 2014:6463-6.
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    ABSTRACT: Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level-set algorithms often assume piecewise constant (PC) or piecewise smooth (PS) for segments, which are implausible for general medical image segmentation. Further, low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms. Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region density distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and background. The region-based statistical model in our algorithm can intuitively provide better performance on noisy images. We constructed a weighted probability map on graphs to incorporate spatial indications from user input with a contextual constraint based on the minimization of contextual graphs energy functional. We measured the performance of our approach on 10 noisy synthetic images and 58 medical datasets with heterogeneous intensities and ill-defined boundaries and compared our technique to the Chan-Vese (CV) region-based level set model, the geodesic active contour model with distance regularization (GACD) and the random walker (RW) model. Our method consistently achieved the highest Dice similarity coefficient (DSC) when compared to the other methods.
    IEEE transactions on bio-medical engineering 07/2014; · 2.15 Impact Factor
  • Biomedical Signal Processing and Control 07/2014; · 1.53 Impact Factor
  • Tong Zhang, Yong Xia, David Dagan Feng
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    ABSTRACT: The hidden Markov random field (HMRF) model has been widely used in image segmentation, as it provides a spatially constrained clustering scheme on two sets of random variables. However, in many HMRF-based segmentation approaches, both the latent class labels and statistical parameters have been estimated by deterministic techniques, which usually lead to local convergence and less accurate segmentation. In this paper, we incorporate the immune inspired clonal selection algorithm (CSA) and Markov chain Monte Carlo (MCMC) method into HMRF model estimation, and thus propose the HMRF–CSA algorithm for brain MR image segmentation. Our algorithm employs a three-step iterative process that consists of MCMC-based class labels estimation, bias field correction and CSA-based statistical parameter estimation. Since both the MCMC and CSA are global optimization techniques, the proposed algorithm has the potential to overcome the drawback of traditional HMRF-based segmentation approaches. We compared our algorithm to the state-of-the-art GA–EM algorithm, deformable cosegmentation algorithm, the segmentation routines in the widely-used statistical parametric mapping (SPM) software package and the FMRIB software library (FSL) on both simulated and clinical brain MR images. Our results show that the proposed HMRF–CSA algorithm is robust to image artifacts and can differentiate major brain structures more accurately than other three algorithms.
    Biomedical Signal Processing and Control 07/2014; 12:10–18. · 1.53 Impact Factor
  • Zhen Liang, Bingang Xu, Zheru Chi, David D Feng
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    ABSTRACT: Saliency models have been developed and widely demonstrated to benefit applications in computer vision and image understanding. In most of existing models, saliency is evaluated within an individual image. That is, saliency value of an item (object/region/pixel) represents the conspicuity of it as compared with the remaining items in the same image. We call this saliency as absolute saliency, which is uncomparable among images. However, saliency should be determined in the context of multiple images for some visual inspection tasks. For example, in yarn surface evaluation, saliency of a yarn image should be measured with regard to a set of graded standard images. We call this saliency the relative saliency, which is comparable among images. In this paper, a study of visual attention model for comparison of multiple images is explored, and a relative saliency model of multiple images is proposed based on a combination of bottom-up and top-down mechanisms, to enable relative saliency evaluation for the cases where other image contents are involved. To fully characterize the differences among multiple images, a structural feature extraction strategy is proposed, where two levels of feature (high-level, low-level) and three types of feature (global, local-local, local-global) are extracted. Mapping functions between features and saliency values are constructed and their outputs reflect relative saliency for multiimage contents instead of single image content. The performance of the proposed relative saliency model is well demonstrated in a yarn surface evaluation. Furthermore, the eye tracking technique is employed to verify the proposed concept of relative saliency for multiple images.
    IEEE transactions on cybernetics. 06/2014;
  • Tong Zhang, Yong Xia, David Dagan Feng
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    ABSTRACT: Statistical classification of voxels in brain magnetic resonance (MR) images into major tissue types plays an important role in neuroscience research and clinical practices, in which model estimation is an essential step. Despite their prevalence, traditional techniques, such as the expectation–maximization (EM) algorithm and genetic algorithm (GA), have inherent limitations, and may result in less-accurate classification. In this paper, we introduce the immune-inspired clonal selection algorithm (CSA) to the maximum likelihood estimation of the Gaussian mixture model (GMM), and thus propose the GMM-CSA algorithm for automated voxel classification in brain MR images. This algorithm achieves simultaneous voxel classification and bias field correction in a three-stage iterative process under the CSA framework. At each iteration, a population of admissible model parameters, voxel labels and estimated bias field are updated. To explore the prior anatomical knowledge, we also construct a probabilistic brain atlas for each MR study and incorporate the atlas into the classification process. The GMM-CSA algorithm has been compared to five state-of-the-art brain MR image segmentation approaches on both simulated and clinical data. Our results show that the proposed algorithm is capable of classifying voxels in brain MR images into major tissue types more accurately.
    Neurocomputing 06/2014; 134:122–131. · 2.01 Impact Factor
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    ABSTRACT: Hopfield Neural Network (HNN) has been demonstrated to be an effective tool for Spectral Mixture Analysis (SMA). However, the spectrum of pure ground objects, known as endmember, must be known previously. In this paper, the HNN is utilized to solve unsupervised SMA, in which Endmember Extraction (EE) and Abundance Estimation (AE) are performed iteratively. Two different HNNs are constructed to solve such multiplicative updating procedure, respectively. The proposed HNN based unsupervised SMA framework is then applied to solve three second-order constrained Nonnegative Matrix Factorization (NMF) models for SMA, including Minimum Distance Constrained NMF (MDC-NMF), Minimum endmember-wise Distance Constrained NMF (MewDC-NMF), and Minimum Dispersion Constrained NMF (MiniDisCo-NMF). As a result, our proposed HNN based algorithms are able to perform unsupervised SMA and extract virtual endmembers without assuming the presence of spectrally pure constituents in highly mixed hyperspectral data. Experimental results on both synthetic and real hyperspectral images demonstrate that our proposed HNN based algorithms clearly outperform traditional Projected Gradient (PG) based solutions for these constrained NMF based SMA.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 06/2014; 7(6):1922-1935. · 2.83 Impact Factor
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    ABSTRACT: Many approaches to facial expression recognition utilize only one type of features at a time. It can be difficult for a single type of features to characterize in a best possible way the variations and complexity of realistic facial expressions. In this paper, we propose a spectral embedding based multi-view dimension reduction method to fuse multiple features for facial expression recognition. Facial expression features extracted from one type of expressions can be assumed to form a manifold embedded in a high dimensional feature space. We construct a neighborhood graph that encodes the structure of the manifold locally. A graph Laplacian matrix is constructed whose spectral decompositions reveal the low dimensional structure of the manifold. In order to obtain discriminative features for classification, we propose to build a neighborhood graph in a supervised manner by utilizing the label information of training data. As a result, multiple features are able to be transformed into a unified low dimensional feature space by combining the Laplacian matrix of each view with the multiview spectral embedding algorithm. A linearization method is utilized to map unseen data to the learned unified subspace. Experiments are conducted on a set of established real-world and benchmark datasets. The experimental results provide a strong support to the effectiveness of the proposed feature fusion framework on realistic facial expressions.
    Neurocomputing 04/2014; 129:136–145. · 2.01 Impact Factor
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    ABSTRACT: Positron emission tomography (PET) images usually suffer from poor signal-to-noise ratio (SNR) due to the high level of noise and low spatial resolution, which adversely affect its performance for lesion detection and quantification. The complementary information present in high-resolution anatomical images from multi-modality imaging systems could potentially be used to improve the ability to detect and/or quantify lesions. However, previous methods that use anatomical priors usually require matched organ/lesion boundaries. In this study, we investigated the use of anatomical information to suppress noise in PET images while preserving both quantitative accuracy and the amplitude of prominent signals that do not have corresponding boundaries on computerized tomography (CT). The proposed approach was realized through a postreconstruction filter based on the nonlocal means (NLM) filter, which reduces noise by computing the weighted average of voxels based on the similarity measurement between patches of voxels within the image. Anatomical knowledge obtained from CT was incorporated to constrain the similarity measurement within a subset of voxels. In contrast to other methods that use anatomical priors, the actual number of neighboring voxels and weights used for smoothing were determined from a robust measurement on PET images within the subset. Thus, the proposed approach can be robust to signal mismatches between PET and CT. A 3-D search scheme was also investigated for the volumetric PET/CT data. The proposed anatomically guided median nonlocal means filter (AMNLM) was first evaluated using a computer phantom and a physical phantom to simulate realistic but challenging situations where small lesions are located in homogeneous regions, which can be detected on PET but not on CT. The proposed method was further assessed with a clinical study of a patient with lung lesions. The performance of the proposed method was compared to Gaussian, edge-preserving bilateral and NLM filters, as well as median nonlocal means (MNLM) filtering without an anatomical prior. The proposed AMNLM method yielded improved lesion contrast and SNR compared with other methods even with imperfect anatomical knowledge, such as missing lesion boundaries and mismatched organ boundaries.
    IEEE Transactions on Medical Imaging 03/2014; 33(3):636-50. · 3.80 Impact Factor
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    ABSTRACT: Due to the imperfections of the radio frequency (RF) coil or object-dependent electrodynamic interactions, magnetic resonance (MR) images often suffer from a smooth and biologically meaningless bias field, which causes severe troubles for subsequent processing and quantitative analysis. To effectively restore the original signal, this paper simultaneously exploits the spatial and gradient features of the corrupted MR images for bias correction via the joint entropy regularization. With both isotropic and anisotropic total variation (TV) considered, two nonparametric bias correction algorithms have been proposed, namely IsoTVBiasC and AniTVBiasC. These two methods have been applied to simulated images under various noise levels and bias field corruption and also tested on real MR data. The test results show that the proposed two methods can effectively remove the bias field and also present comparable performance compared to the state-of-the-art methods.
    Bio-medical materials and engineering 01/2014; 24(1):1239-45. · 0.85 Impact Factor
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    ABSTRACT: Parametric FDG-PET images offer the potential for automated identification of the different dementia syndromes. However, various existing image features and classifiers have their limitations in characterizing and differentiating the patterns of this disease. We reported a hybrid feature extraction, selection, and classification approach, namely, the GA-MKL algorithm, for separating patients with suspected Alzheimer's disease and frontotemporal dementia from normal controls. In this approach, we extracted three groups of features to describe the average level, spatial variation, and asymmetry of glucose metabolic rates in 116 cortical volumes. An optimal combination of features, that is, capable of classifying dementia cases was identified by a genetic algorithm- (GA-) based method. The condition of each FDG-PET study was predicted by applying the selected features to a multikernel learning (MKL) machine, in which the weighting parameter of each kernel function can be automatically estimated. We compared our approach to two state-of-the-art dementia identification algorithms on a set of 129 clinical cases and improved the performance in separating the dementia types, achieving accuracy of 94.62%. There is a very good agreement between the proposed automated technique and the diagnosis made by clinicians.
    BioMed research international. 01/2014; 2014:421743.
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    ABSTRACT: Medical images typically exhibit complex feature space distributions due to high intra-class variation and inter-class ambiguity. Monolithic classification models are often problematic. In this study, we propose a novel Large Margin Local Estimate (LMLE) method for medical image classification. In the first step, the reference images are subcategorized, and local estimates of the test image are computed based on the reference subcategories. In the second step, the local estimates are fused in a large margin model to derive the similarity level between the test image and the reference images, and the test image is classified accordingly. For evaluation, the LMLE method is applied to classify image patches of different interstitial lung disease (ILD) patterns on high-resolution computed tomography (HRCT) images. We demonstrate promising performance improvement over the state-of-the-art.
    01/2014; 17(Pt 2):196-203.

Publication Stats

773 Citations
143.76 Total Impact Points


  • 2014
    • Budapest University of Technology and Economics
      • Department of Control Engineering and Information Technology
      Budapeŝto, Budapest, Hungary
  • 2000–2014
    • University of Sydney
      • • School of Information Technologies
      • • Biomedical and Multimedia Information Technology Research Group (BMIT)
      Sydney, New South Wales, Australia
  • 2012
    • Nanjing University of Science and Technology
      • School of Computer Science and Technology
      Nanjing, Jiangsu Sheng, China
  • 2009–2012
    • Shanghai Jiao Tong University
      • School of Medicine
      Shanghai, Shanghai Shi, China
    • Westmead Hospital
      • Department of Medical Physics
      Sydney, New South Wales, Australia
  • 2007–2012
    • Royal Prince Alfred Hospital
      • Department of Pet & Nuclear Medicine
      Camperdown, New South Wales, Australia
    • National ICT Australia Ltd
      Sydney, New South Wales, Australia
    • Tsinghua University
      • Department of Biomedical Engineering
      Beijing, Beijing Shi, China
  • 1998–2012
    • The Hong Kong Polytechnic University
      • Department of Electronic and Information Engineering
      Hong Kong, Hong Kong
  • 2011
    • City University London
      • Centre for Health Informatics
      Londinium, England, United Kingdom
  • 2000–2011
    • Northwestern Polytechnical University
      • Department of Computer Science and Software
      Xi’an, Liaoning, China
  • 2010
    • Soochow University (PRC)
      • Department of Electronic Engineering
      Wu-hsien, Jiangsu Sheng, China
  • 2002–2003
    • City University of Hong Kong
      • Department of Electronic Engineering
      Kowloon, Hong Kong
  • 1999–2001
    • The University of Hong Kong
      • Department of Electrical and Electronic Engineering
      Hong Kong, Hong Kong