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| 1 | The design of a CORBA - based PACS in three -tier architecture显示文摘 | Li Zhuo Wu Lingda Wei Yingmei | 2005 | Proc SPIE Med Imaging2005，5748，： | 1 |
| 2 | Directional Zernike moments for rotation-free recognition of online sketched symbols显示文摘 | Zhang Yougen Wu Lingda Song Hanchen | 2013 | Electronics Letters2013，49，16： | 1 |
| 3 | 3D representation of radar coverage in complicated environment显示文摘 | Chen Peng Wu Lingda | 2008 | Simulation Modelling Practice and Theory2008，16，9： | 1 |
| 4 | A novel unsupervised approach for multilevel image clustering from unordered image collection显示文摘 | Lai KANG Lingda WU Yee-Hong YANG | 2013 | Frontiers of Computer Science2013，7，1： | 1 |
| 5 | 3D representation of radar coverage in complex environment显示文摘 | CHEN Peng WU Lingda | 2007 | International journal of computer science and network security (IJCSNS)2007，7，7： | 1 |
| 6 | Interactive multigraph visualization and exploration with a two-phase strategy显示文摘While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph, a clear layout representing a global structure is of great importance, and interactive visual analysis which allows the multiple edges to be adjusted in appropriate ways for detailed presentation is also essential. A novel interactive two-phase approach to visualizing and exploring multigraph is proposed. The approach consists of two phases: the first phase improves the previous popular works on force-directed methods to produce a brief drawing for the aggregation graph of the input multigraph, while the second phase proposes two interactive strategies, the magnifier model and the thematic-oriented subgraph model. The former highlights the internal details of an aggregation edge which is selected interactively by user, and draws the details in a magnifying view by cubic Bezier curves; the latter highlights only the thematic subgraph consisting of the selected multiple edges that the user concerns. The efficiency of the proposed approach is demonstrated with a real-world multigraph dataset and how it is used effectively is discussed for various potential applications. | Huaquan Hu Lingda Wu Chao Yang Hanchen Song | 2014 | Journal of Systems Engineering and Electronics2014，25，5： | 1 |
| 7 | Efficient View-dependent Modeling and Rendering of Large-scale Ocean Wave Based on Digital Earth显示文摘 | Li Sujun Yang Bing Wu Lingda | 2007 | International Journal of Computer Science and Network Security2007，7，5： | 1 |
| 8 | Depth estimation using an improved stereo network显示文摘Self-supervised depth estimation approaches present excellent results that are comparable to those of the fully supervised approaches,by employing view synthesis between the target and reference images in the training data.ResNet,which serves as a backbone network,has some structural deficiencies when applied to downstream fields,because its original purpose was to cope with classification problems.The low-texture area also deteriorates the performance.To address these problems,we propose a set of improvements that lead to superior predictions.First,we boost the information flow in the network and improve the ability to learn spatial structures by improving the network structures.Second,we use a binary mask to remove the pixels in low-texture areas between the target and reference images to more accurately reconstruct the image.Finally,we input the target and reference images randomly to expand the dataset and pre-train it on ImageNet,so that the model obtains a favorable general feature representation.We demonstrate state-of-the-art performance on an Eigen split of the KITTI driving dataset using stereo pairs. | Wanpeng XU Ling ZOU Lingda WU Yue QI Zhaoyong QIAN | 2022 | Frontiers of Information Technology & Electronic Engineering2022，23，5： | 0 |
| 9 | Interactive Details on Demand Visual Analysis on Large Attributed Networks显示文摘Increasing scale leaves a challenging problem for visualizing large attributed networks. This paper proposes a details on demand approach for exploratory visual analysis on large attributed networks. Ma jor structures are located and emphasized at each level, providing clues for user observation. The detailed subnet structure emerges gradually through the exploration process.Our method dynamically aggregates network with consideration of both structural and attribute properties. It allows a flexible control of the hierarchy structure. A userspecified interaction strategy is introduced to enable users to customize the analysis flow according to different analytic tasks. Case studies demonstrate that the proposed method is effective in extracting global knowledge, locating ma jor structures, and discovering hidden information in networks. | DU Xiaolei WEI Yingmei WU Lingda | 2018 | Chinese Journal of Electronics2018，27，5： | 0 |
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