2017 Imaging Science Camp
2017/03/10-2017/03/12
Contributed Talks
Contributed Talks
Poster presentations
Mr. CHAN Cheuk Kit Kelvin, The Chinese University of Hong Kong
Title: Edge-histogram specification via convex optimization
Abstract: We introduce an algorithm to achieve edge-histogram specification of images. Our algorithm consists of two parts. First, we make use of the idea from Nikolova et al. to achieve exact edge-histogram specification, in which the edge-histogram of an input image will be modified according a target distribution, either a pre-defined distribution or a distribution estimated from a target image. After modification, a simple projected gradient descent algorithm is used to solve a constrained linear convex optimization problem. We also demonstrate several applications of our algorithm including detail enhancement and detail extraction.
Ms. FANG Yingying, Wuhan University
Title: Reversible Data Hiding Using Non-local Means Prediction
Abstract: We propose a prediction-error based reversible data hiding scheme by incorporating non-local means (NLM) prediction. The traditional local predictors rely on the local correlation and behave badly in predicting textural pixels. By globally utilizing the potential self-similarity contained in the image itself, NLM can achieve better prediction in texture regions.
Mr. KAN Kai Fung, The Chinese University of Hong Kong
Title: Spectral-spatial classification algorithm for high-resolution fused hyperspectral and LiDAR imagery
Abstract: A spectral-spatial classification scheme for high-resolution fused hyperspectral and LiDAR imagery is introduced. In particular, we propose a semi-supervised classification algorithm to exploit both spectral and spatial information to carry out the classification. The algorithm consists of three steps, (i) cluster the data by using nonnegative matrix factorization (NMF), (ii) perform pixel-wise classification on the data by using convolutional neural network (CNN) and (iii) obtain the final classification result by performing majority vote on each cluster and post-regularization. The proposed algorithm gives higher classification accuracy and more smooth regions when compared to pixel-wise classification.
Mr. KWAN Wing Fai, The Hong Kong University of Science and Technology
Title: An Efficient Numerical Method for Paraxial Multi-Color Optical Self-Focusing in Nematic Liquid Crystals
Abstract: We propose a numerically efficient algorithm for simulating the multi-color optical self-focusing phenomena in nematic liquid crystals. The beam propagation is modeled by a parabolic wave equation coupled with a nonlinear elliptic partial differential equation governing the angle between the crystal and the direction of propagation. The paraxial parabolic wave equation is solved by a fast Huygens sweeping method modified from a related algorithm, while the nonlinear elliptic PDE is handled by the alternating direction explicit (ADE) method. The overall algorithm is shown to be numerically efficient, for computing high frequency beam propagations.
Mr. LI Jizhou, The Chinese University of Hong Kong
Title: PURE-LET image deconvolution
Abstract: We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parametrize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds (LET). This parametrization is then optimized by minimizing a robust estimate of the mean squared error, the “Poisson unbiased risk estimate (PURE)”. Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients. In contrast to existing approaches, the proposed algorithm merely amounts to solving a linear system of equations which has a fast and exact solution. Simulation experiments over various noise levels indicate that the proposed method outperforms current state-of-the-art techniques, in terms of both restoration quality and computational time.
Mr. LIU Nan, The Chinese University of Hong Kong
Title: A Fast Method of Multi-frame Super-resolution Reconstruction
Abstract: We proposed a fast method to obtain the high-resolution image from multiple low-resolution frames. The motions between low-resolution frames are estimated by optical flow, which could accurately register the low-resolution frames to the reference frame. Instead of reconstructing the high-resolution image by minimizing an optimization functional, we consider a locally linear representation of the high-resolution image by a set of basis functions. This approach can handle the case of high noise level and illumination change. Numerical experiments show the effectiveness of our method.
Mr. NG Yu Keung, The Hong Kong University of Science and Technology
Title: Visualizing Continuous Dynamical System using Finite Time Lyapunov Exponent from limited Lagrangian trajectories data
Abstract: The Lagrangian Coherent Structure (LCS) are useful tool to visualize and extract useful information in complex continuous dynamical system. The structure of LCS can be extracted from finite-time Lyapunov exponent (FTLE). However, in order to compute FTLE, the particles’ behavior in whole domain are needed, which is not applicable in reality. We propose a way to compute the FTLE with limited information on the particles’ behavior by Radial Basis Functions. We find that the major features of FTLE can be reconstructed for the cases of aperiodic, periodic, and autonomous flow.
Mr. SHI Yun, The Hong Kong Polytechnic University
Title: Image Reconstruction with Folded Concave Regularized Total Variation
Abstract: In tackling the conventional image process tasks such as deblurring and denoising, sparse optimization techniques are often used. Specifically, the solution to the total variation regularized 
minimisation problem are considered to be able to provide satisfying approximation to the true image. In this study, we adapted a folded concave envelope onto the conventional total variation, and showed that by choosing the appropriate initial points and regularisation path, we can achieve statistically more accurate restoration results compare to that of the convex total variation regularized model.
Dr. XU Boxi, The Hong Kong University of Science and Technology
Title: A numerical method for the fractional Laplacian in the high-dimensional space
Abstract: The fractional Laplacian is used to describe the spatial non-locality and power law behaviors of scientific and engineering problems in the recent decades. It is mathematically too complicated and computationally very expensive when applied to higher dimensional cases. In this report, we reduce the high-dimensional fractional Laplacian to the lower dimensional ones, which is easier for numerical discretization. A numerical method is proposed for these operators, and some numerical simulations are shown here to explain the effectiveness of this method.
Oral presentations
Mr. AU YEUNG Tak Shing, The Chinese University of Hong Kong
Title: Numerical inversion of geodesic X-ray transforms arising from Traveltime tomography
Abstract: In this work, we consider the inverse problem of computing the unknown function f on domain from its geodesic X-ray transform. The method is related to the Neumann series based numerical method which is developed for photoacoustic tomography in a paper by Qian, Stefanov, Uhlmann, and Zhao and also the idea of layer stripping algorithm by Uhlmann and Vasy. Neumann series based numerical method is an efficient and convergent numerical scheme that recovers the initial condition of an acoustic wave equation with non-constant sound speeds by boundary measurements. The layer stripping algorithm we used is to separate the domain into small partitions and then recover f on each small partition by using Neumann series based numerical method from its geodesic X-ray data, starting with the outermost region. Combining the above methodologies, the resulting algorithm can undergo the reconstruction layer by layer so to prevent full inversion of the whole domain. Thus, it is very efficient and consumes much less computational time. Numerical examples including isotropic metrics and the Marmousi synthetic model are shown to validate the new numerical method.
Mr. CAI Zhenfeng, University of Macau
Title: Application of Biquaternionic analytic signal in envelope detection in 3D imaging
Abstract: The analytic signal is a useful mathematical tool. It separates qualitative and quantitative information of an image in form of the local phase and local amplitude. The Clifford Fourier transform (CFT) plays a vital role in the representation of multidimensional signals, by generalizing the CFT to the Clifford linear canonical transform (CLCT), we present a new type of biquaternionic analytic signal. With the help of this new method, the envelop detection problems of 3D images can get a better visual appearance of the needle.
Mr. CHAN Hei Long, The Chinese University of Hong Kong
Title: Topology Preserving Image Segmentation by Beltrami Representation of Shapes
Abstract: A new approach using the Beltrami Signature of a shape to segment an object from an image with promised topology is proposed in this paper. Given a target image, a template image called the topological prior image is deformed according to the newly proposed Beltrami Signature, such that the topology of the segmented region is preserved as that of the object interior in the topological prior image. The topology preserving property of the deformation is guaranteed by imposing only one constraint on the Beltrami Signature, which is easy to be handled. Introducing the Beltrami Signature also allows large deformations on the topological prior image, so that it can be a very simple image, such as an image of disks, torus, disjoint disks, etc. Hence, prior shape information of target image is unnecessary for the proposed model. Additionally, the proposed model can be easily incorporated with selective segmentation, in which landmark constraints can be imposed interactively to facilitate any practical need (e.g. medical imaging). High accuracy and stability of the proposed model to deal with different segmentation tasks is validated by numerical experiments on both artificial and real images.
Ms. CHEN Xinshi, The Chinese University of Hong Kong
Title: Parametric FEM for shape optimization applied to Golgi stacks
Abstract: The objects I am concerned with are the vesicles with closed surfaces without topological changes. The shape evolution of the vesicles is studied, when the elastic energy is considered as the dominated energy. Governed by the elastic energy, the geometric evolution equation is formulated based on shape differential calculus and then implemented by parametric Finite Element Method. There are four main points in the model. First, the elastic energy is replaced by Willmore energy to form the geometric equation by Willmore flow. Second, the surface area is set to be conserved. This constraint is formulated as the mean curvature flow and endorsed into the model by Lagrange Multiplier Method. Third, as our model is applied to mimic the growing process of Golgi stack, we form two boundaries above and below the vesicles in our model to mimic the constraints from Golgi matrix. This part is modeled by using a Heaviside function. Forth, to couple multiple vesicles together in one model and avoid the intersection of the vesicles, a special distance function is also applied to the model.
Mr. CHEN Yun, Sun Yat-sen University
Title: Regularized CT reconstruction on unstructured grid
Abstract: Computed tomographic (CT) reconstruction is an ill-posed inverse problem. Reconstruction on unstructured grid alleviates the ill-posedness and reduces the computational cost by decreasing the dimension of the solution space. In addition, regularization method has been proven to be an effective method to suppress noise and overcome the ill-posedness of CT reconstruction problem. However, there is no systematic study on edge-preserving regularization methods for tomography reconstruction on unstructured grid. In this paper, we propose a new regularized CT reconstruction method on unstructured grid. The proposed regularized CT reconstruction method is modeled as a three-term optimization problem, containing a weighted least square fidelity term motivated by the simultaneous algebraic reconstruction technique (SART). The related cost function contains two non-differentiable terms which bring difficulty to the development of the fast solver. To overcome this difficulty, a fixed-point proximity algorithm with SART (FPPA-SART) is developed for solving the related non-smooth convex optimization problem. Convergence analysis is performed for the proposed iterative algorithm. Finally, we compare the proposed regularized CT reconstruction method to SART with no regularization on unstructured grid, SART with quadratic regularization on unstructured grid, and FPPA-SART with the discrete total variation (TV) regularization in pixel domain, using numerical simulation data and clinical data with different noise levels. Numerical experiments demonstrate that the proposed regularized CT reconstruction method on unstructured grid is effective to suppress noise and preserve edge features. In particular, compared to the reconstruction in pixel domain, the proposed regularized CT reconstruction method on unstructured grid performs better to reduce the computational cost and suppress noise.
Mr. CHENG Dong, University of Macau
Title: Monogenic Signal Associated with Linear Canonical Transform and Application to Edge Detection Problems
Abstract: Monogenic signal is regarded as a generalization of analytic signal from one dimensional to higher dimensional space. It is defined by an original signal with the combination of Riesz transform. Then it provides the signal features representation, such as the local attenuation and the local phase vector. The main objective of this study is to analyze the local phase vector and the local attenuation in the higher dimensional spaces. The differential phase congruency is applied in the edge detection problems.
Mr. GENG Mingmeng, Southern University of Science and Technology of China
Title: PatchMatch Transfer for Light Field Editing
Abstract: Plenoptic photography is becoming increasingly popular. The image processing operations are different between 2D images and light field images. In this paper, we use PatchMatch method, a randomized correspondence algorithm for structural image editing, to transfer our editing operations. Based on PatchMatch method, we build a confidence map for each pixel in different views in light field images at first. Then we use depth to enforce spatial coherence and develop a novel method for light field editing. Finally, in order to evaluate the accuracy of the editing operations, we develop a measurement for light field editing. Several experimental results on some datasets are also shown, demonstrating the effectiveness of our method.
Ms. HU Xiao Xiao, University of Macau
Title: Image Enhancement and Edge Detection based on the Quaternion Analytic Signal
Abstract: Quaternion analytic signal (QAS) is regarded as a generalization of analytic signal from 1D to 4D space. It is defined by an original signal with its quaternion partial and total Hilbert transforms. The QAS provides the signal features representation, such as the local attenuation and local phase vector. The aim of the present study is twofold. Firstly, it attempts to analyze the relationship between the local phase vector and the local attenuation in 4D space by the generalized Cauchy−Riemann equations. Secondly, we present various types of edge detection filters, which are connected with the GAS and local phase vector. Comparisons with competing methods on real-world images consistently show the superiority of the proposed methods.
Mr. KE Rihuan, The Chinese University of Hong Kong
Title: Fast Wavefront Reconstruction Method in Adaptive Optics
Abstract: The atmospheric turbulence is one of the most important factors that limit the image resolution of ground based telescope. For large ground based telescopes, there is a requirement on high speed wavefront reconstruction from wavefront sensor measurements. For example, the Extremely Large Telescope (ELT) produces a problem with number of unknowns can be as large as 104 - 105. Some reconstruction methods that work well in small telescopes may fail in this case. Here, the reconstructors with linear time complexity are considered.
Mr. LAI Yu Hin, The Chinese University of Hong Kong
Title: Image Retargeting by Beltrami Differential Model
Abstract: We present a novel approach for interactive content-aware image resizing. The warping of image domain is content-aware. The resizing is performed on the triangular mesh over the image, which captures the image geometry information as well as the underlying image features. The warped triangular mesh and the horizontal and vertical scales of all triangles are simultaneously obtained by a quadratic optimization which can be achieved by solving a sparse linear system. Our approach can preserve the shapes of curved features in the resized images. The resizing operation can be performed in an interactive rate which makes the proposed approach practically useful for real-time image resizing. To guarantee fold-over free resizing result, we modify the optimization to a standard quadratic programming. A number of experimental results have shown that our approach has obtained pleasing results and outperforms the previous approaches.
Mr. LAM Chi Yeung, The Chinese University of Hong Kong
Title: Discontinuous Galerkin methods for high frequency wave propagation
Abstract: The numerical approximation of high-frequency wave propagation in heterogeneous media is a challenging problem. In particular, one could not cope with obtaining accurate high-frequency solutions by direct simulations, as they require several points per wavelength for stability and usually require many points per wavelength for a satisfactory accuracy. In this talk, we will discuss some aspect on using discontinuous Galerkin methods to compute for high-frequency wave solutions.
Mr. LAM Ming Fai, The Chinese University of Hong Kong
Title: A staggered discontinuous Galerkin method for the nonlinear Stokes system with symmetric stress tensor
Abstract: We present a staggered discontinuous Galerkin (SDG) method for solving nonlinear Stokes system with symmetric stress tensor in two dimensions. The problem is discretized by staggered DG spaces, which preserve the structures of the continuous problem, and also provide local and global conservation properties. The nonlinear term is due to the presence of the variable viscosity, which is crucial in incompressible non-Newtonian fluid flow problems. To solve the nonlinear equations, Newton’s method is chosen due to its quadratic convergence rate. We analyze the stability of the SDG method, and give a priori error estimates. Numerical experiments are included to validate our theoretical estimates, and to show the performance of the method.
Mr. LAU Chun Pong, The Chinese University of Hong Kong
Title: Restoration of Atmospheric Turbulence-distorted images via RPCA and Quasiconformal Maps
Abstract: We address the problem of restoring a high-quality image from an observed image sequence strongly distorted by atmospheric turbulence. A novel algorithm is proposed in this paper to reduce geometric distortion as well as space and time-varying blur due to strong turbulence. By considering an optimization problem, our algorithm first obtains a sharp reference image and a sub-sampled image sequence containing sharp and mildly distorted image frames with respect to the reference image. The sub-sampled image sequence is then stabilized by applying the Robust Principal Component Analysis (RPCA) on the deformation fields between image frames and warping the image frames by a quasiconformal map associated to the low-rank part of the deformation matrix. After image frames are registered to the reference image, the low-rank part of them are deblurred via a blind deconvolution, and the deblurred frames are then fused with the enhanced sparse part. Experiments have been carried out on both synthetic and real turbulence-distorted video. Results demonstrate our method is effective in alleviating distortions and blur, restoring image details and enhancing visual quality.
Mr. LEUNG LIU Yusan, The Chinese University of Hong Kong
Title: Conformal Welding of Boundaries
Abstract: Conformal welding considers the correspondence between the boundaries of two disks and tries to construct a closed surface by joining the two disks in a conformal way so that they match on the boundaries. Instead of joining the whole boundaries between two shapes, we consider the problem of conformal welding along some part of the boundaries. We propose a variant of the zipper algorithm to achieve the goal. Possible applications include spherical conformal parameterization for distributed 3D image warping.
Mr. LI Chor Hung, The Chinese University of Hong Kong
Title: Residual-driven online generalized multiscale finite element methods
Abstract: The construction of local reduced-order models via multiscale basis functions has been an area of active research. In this paper, we propose online multiscale basis functions which are constructed using the offline space and the current residual. Basically the offline spaces are constructed using Generalized Multiscale Finite Element Methods (GMsFEM). The method is used here to solve the high-contrast flow problem with non-homogeneous Dirichlet boundary condition. We present numerical results and graphs to discuss the accuracy of this method in solving this kind of equation.
Mr. MAK Hugo Wai Leung, The Hong Kong University of Science and Technology
Title: Tracking irregular shaped moving particles via Image Processing techniques and its application in Biology
Abstract: Biological images, frames and videos often involve movements of more than one animal or individual: individuals may touch each other, move in paths that cross over each other, or even there may be self-intersection occurring within each individual. In literature, IDL method was adopted for image processing and tracking positions of each individual, and in this presentation, we will introduce a software called the idTracker as an alternative way for imaging analysis. Examples related to biology and physics will be provided to evaluate the effectiveness of these two methods, and we will extend to the discussion of errors brought out in particle tracking. The errors include static and dynamics errors, which will propagate and result in random assignment of positions and momentum in future time, therefore obtaining ways for minimizing all these errors are necessary. Shape of investigated particles or individuals is also another important factor affecting the formation of eventual probability density distribution. The respective mathematical theories and principles will be discussed as well.
Ms. MO Qianping, The Chinese University of Hong Kong
Title: Geometric Tight Frame Color Model for Authentication of van Gogh Paintings
Abstract: This talk proposes a geometric tight frame color model to authenticate van Gogh paintings from imitations. The model extracts color features from the paintings using the HSV color space of the 2-level tight frame transform of the paintings. With these color information, a small set of color features is selected using forward stage-wise rank boosting. A classification algorithm under leave-one-out cross-validation procedure is implemented. On a dataset of 79 paintings given by the van Gogh Museum and Kröller-Müller Museum, our results show that the classification accuracies achieve 95% using only 8 features chosen from our algorithm. The result reflects that the hue information plays an important role in van Gogh painting authentication.
Mr. PUN Sai Mang, The Chinese University of Hong Kong
Title: Goal-oriented adaptivity of mixed GMsFEM for flows in heterogeneous media
Abstract: We present an adaptive goal-oriented framework with the mixed Generalized Multiscale Finite Element Method (GMsFEM) to numerically solve the flow problem in heterogeneous media. In this paper, three different error indicators are considered: two, goal-oriented, and one a standard adaptive offline method. Each is used to approximate a number of quantities of interest given by interior edge fluxes in a high-contrast field. Following the setting of mixed GMsFEM, on each coarse neighborhood of the computational domain, the local error indicator introduced here is computed by the product of the residual norms of the primal and dual variational equations over coarse neighborhoods. Numerical results demonstrate the efficiency of this goal-oriented method in comparison to a multiscale implementation of the dual weighted residual method, and a standard adaptive offline method. This is the joint work with Prof. Eric Chung and Prof. Sara Pollock.
Mr. QIU Di, The Chinese University of Hong Kong
Title: A comparison study of shape interpolation algorithms with controlled conformality distortion
Abstract: We aim to give an overview and details of the algorithms utilizing Teichmüller mapping methods in shape analysis, that is a quasiconformal mapping f: S1 ~ S2 between two shapes, f = h1*Ak*g where Ak is an affine mapping, and h, g are conformal mappings from the shapes to the complex plane. And we will specifically focus on the problem of shape interpolation. We shall also compare several existing algorithms, namely: 1. Iterative smoothing and renormalising operations on the Beltrami coefficients; 2. Minimising the L2 Beltrami energy of the mapping via alternating descent.
Mr. WANG Chao, The Chinese University of Hong Kong
Title: Nonconvex optimization on rotating point spread methods with application on microscopy and telescope imaging
Abstract: The astigmatism-based method is a promising technique for three-dimensional (3D) imaging. By adding phase mask in microscopy or telescope, the astigmatic 2D image is obtained and contains 3D information. The defocus effect can be modeled as a rotating point spread function (rPSF). One specific rPSF is considered in this project. Estimating 3D localization for point sources is a sparsity problem and can be solved by various strategies of nonconvex optimization.
Mr. YANG Lei, The Hong Kong Polytechnic University
Title: A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems
Abstract: In this talk, we consider a general matrix factorization model, which covers a large class of existing models for many applications in machine learning, imaging sciences and so on. To solve the resulting nonconvex, non-smooth and non-Lipschitz optimization problem, we introduce a potential function specifically constructed for our problem, and derive a non-monotone alternating updating method with a suitable line search scheme, and analyze its convergence. We show that, under some mild conditions, our line search scheme is well-defined and the sequence thus generated is bounded, and then the cluster point of the sequence gives a stationary point of the nonconvex optimization problem. Finally, we perform numerical experiments comparing our method with some existing methods for non-negative matrix factorization and matrix completion on real data. The numerical results show that our method is efficient.
Mr. YING Ningchen, The Hong Kong University of Science and Technology
Title: A weak formulation for Solving the Stokes without body fitting grid
Abstract: We develop an effective interface tracking method to solve the incompressible Stokes flow with moving interfaces. The Stokes equations are first rewritten into elliptic equations with singular sources which can be efficiently solved by a simple weak formulation proposed in (Hou, 2013). The key idea is to first split the solution into a singular part and a regular part additively. The singular part captures the interface conditions, while the regular part approximates the equations in the whole domain, which can be solved by standard finite element formulation. We carefully design numerical methods to interpolate the velocity to the moving interface. We present numerical tests to demonstrate the accuracy and other properties of our method.
Ms. ZHANG Xinxin, The Chinese University of Hong Kong
Title: Iterative Fitting After Elastic Registration: An Efficient Strategy For Accurate Estimation of Parametric Deformations.
Abstract: We propose an efficient method for image registration based on iteratively fitting a parametric model to the output of an elastic registration. It combines the flexibility of elastic registration - able to estimate complex deformations - with the robustness of parametric registration - able to estimate very large displacement. Our approach is made feasible by using the recent Local All-Pass (LAP) algorithm; a fast and accurate filter-based method for estimating the local deformation between two images. Moreover, at each iteration we fit a linear parametric model to the local deformation which is equivalent to solving a linear system of equations (very fast and efficient). We use a quadratic polynomial model however the framework can easily be extended to more complicated models. The significant advantage of the proposed method is its robustness to model mismatch (e.g. noise and blurring). Experimental results on synthetic images and real images demonstrate that the proposed algorithm is highly accurate and outperforms a selection of image registration approaches.
Presentation schedule:
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March 10 |
Poster presentations |
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19:30-20:30 |
CHAN Cheuk Kit Kelvin, The Chinese University of Hong Kong |
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FANG Yingying, Wuhan University |
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KAN Kai Fung, The Chinese University of Hong Kong |
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KWAN Wing Fai, The Hong Kong University of Science and Technology |
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LI Jizhou, The Chinese University of Hong Kong |
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LIU Nan, The Chinese University of Hong Kong |
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NG Yu Keung, The Hong Kong University of Science and Technology |
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SHI Yun, The Hong Kong Polytechnic University |
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XU Boxi, The Hong Kong University of Science and Technology |
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March 11 |
Oral presentations: First session |
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09:15-09:30 |
YANG Lei, The Hong Kong Polytechnic University |
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09:30-09:45 |
CAI Zhenfeng, University of Macau |
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09:45-10:00 |
LAU Chun Pong, The Chinese University of Hong Kong |
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10:00-10:15 |
LAI Yu Hin, The Chinese University of Hong Kong |
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10:15-10:35 |
Tea break |
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10:35-10:50 |
WANG Chao, The Chinese University of Hong Kong |
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10:50-11:05 |
LI Chor Hung, The Chinese University of Hong Kong |
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11:05-11:20 |
MO Qianping, The Chinese University of Hong Kong |
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March 11 |
Oral presentations: Second session |
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19:10-19:25 |
GENG Mingmeng, South University of Science and Technology of China |
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19:25-19:40 |
LAM Chi Yeung, The Chinese University of Hong Kong |
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19:40-19:55 |
CHENG Dong, University of Macau |
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19:55-20:10 |
CHEN Xinshi, The Chinese University of Hong Kong |
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20:10-20:25 |
ZHANG Xinxin, The Chinese University of Hong Kong |
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20:25-20:40 |
PUN Sai Mang, The Chinese University of Hong Kong |
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20:40-20:55 |
MAK Hugo Wai Leung, The Hong Kong University of Science and Technology |
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20:55-21:10 |
CHEN Yun, Sun Yat-sen University |
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March 12 |
Oral presentations: Third session |
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09:10-09:25 |
YING Ningchen, The Hong Kong University of Science and Technology |
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09:25-09:40 |
QIU Di, The Chinese University of Hong Kong |
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09:40-09:55 |
LAM Ming Fai, The Chinese University of Hong Kong |
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09:55-10:10 |
LEUNG LIU Yusan, The Chinese University of Hong Kong |
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10:10-10:30 |
Tea break |
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10:30-10:45 |
AU YEUNG Tak Shing, The Chinese University of Hong Kong |
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10:45-11:00 |
KE Rihuan, The Chinese University of Hong Kong |
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11:00-11:15 |
CHAN Hei Long, The Chinese University of Hong Kong |
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11:15-11:30 |
HU Xiao Xiao, University of Macau |