Now showing items 1-20 of 1435

    • One-step robust deep learning phase unwrapping 

      Wang, Kaiqiang; Li, Ying; Kemao, Qian; Di, Jianglei; Zhao, Jianlin (2019)
      Phase unwrapping is an important but challenging issue in phase measurement. Even with the research efforts of a few decades, unfortunately, the problem remains not well solved, especially when heavy noise and aliasing ...
    • High performance city rendering in Vulkan 

      Zhang, Alex; Chen, Kan; Johan, Henry; Erdt, Marius (2018)
      City scale scenes often contain large amounts of geometry and texture that cannot altogether fit on GPU memory. Our ongoing work seek to minimise texture memory usage by streaming only view-relevant textures and to improve ...
    • Distribution-based semi-supervised learning for activity recognition 

      Qian, Hangwei; Pan, Sinno Jialin; Miao, Chunyan (2019)
      Supervised learning methods have been widely applied to activity recognition. The prevalent success of existing methods, however, has two crucial prerequisites: proper feature extraction and sufficient labeled training ...
    • Phase retrieval for high-speed 3D measurement using binary patterns with projector defocusing 

      Zheng, Dongliang; Kemao, Qian; Da, Feipeng; Seah, Hock Soon (2017)
      Recent digital technology allows binary patterns to be projected with a very high speed, which shows great potential for high-speed 3D measurement. However, how to retrieve an accurate phase with an even faster speed is ...
    • MNPR : a framework for real-time expressive non-photorealistic rendering of 3D computer graphics 

      Montesdeoca, Santiago E.; Seah, Hock Soon; Semmo, Amir; Bénard, Pierre; Vergne, Romain; Thollot, Joëlle; Benvenuti, Davide (2018)
      We propose a framework for expressive non-photorealistic rendering of 3D computer graphics: MNPR. Our work focuses on enabling stylization pipelines with a wide range of control, thereby covering the interaction spectrum ...
    • Towards personalized maps : mining user preferences from geo-textual data 

      Zhao, Kaiqi; Liu, Yiding; Yuan, Quan; Chen, Lisi; Chen, Zhida; Cong, Gao (2016)
      Rich geo-textual data is available online and the data keeps increasing at a high speed. We propose two user behavior models to learn several types of user preferences from geo-textual data, and a prototype system on top ...
    • Revisiting the stop-and-stare algorithms for influence maximization 

      Huang, Keke; Wang, Sibo; Bevilacqua, Glenn; Xiao, Xiaokui; Lakshmanan, Laks V. S. (2017)
      Influence maximization is a combinatorial optimization problem that finds important applications in viral marketing, feed recommendation, etc. Recent research has led to a number of scalable approximation algorithms for ...
    • 3D human motion recovery from a single video using dense spatio-temporal features with exemplar-based approach 

      Leong, Mei Chee; Lin, Feng; Lee, Yong Tsui (2019)
      This study focuses on 3D human motion recovery from a sequence of video frames by using the exemplar-based approach. Conventionally, human pose tracking requires two stages: 1) estimating the 3D pose for a single frame, ...
    • Summarizing static and dynamic big graphs 

      Khan, Arijit; Bhowmick, Sourav Sara; Bonchi, Francesco (2017)
      Large-scale, highly-interconnected networks pervade our society and the natural world around us, including the World Wide Web, social networks, knowledge graphs, genome and scientific databases, medical and government ...
    • PICASSO : exploratory search of connected subgraph substructures in graph databases 

      Huang, Kai; Bhowmick, Sourav Sara; Zhou, Shuigeng; Choi, Byron (2017)
      Recently, exploratory search has received much attention in information retrieval and database fields. This search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data ...
    • An experimental evaluation of point-of-interest recommendation in location-based social networks 

      Liu, Yiding; Pham, Tuan-Anh Nguyen; Cong, Gao; Yuan, Quan (2017)
      Point-of-interest (POI) recommendation is an important service to Location-Based Social Networks (LBSNs) that can benefit both users and businesses. In recent years, a number of POI recommender systems have been proposed, ...
    • A system for region search and exploration 

      Feng, Kaiyu; Zhao, Kaiqi; Liu, Yiding; Cong, Gao (2016)
      With the increasing popularity of mobile devices and location based services, massive amount of geo-textual data (e.g., geo-tagged tweets) is being generated everyday. Compared with traditional spatial data, the textual ...
    • Medusa : a parallel graph processing system on graphics processors 

      Zhong, Jianlong; He, Bingsheng (2014)
      Medusa is a parallel graph processing system on graphics processors (GPUs). The core design of Medusa is to enable developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential ...
    • Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression 

      Xiang, Qikun; Nevat, Ido; Zhang, Pengfei; Zhang, Jie (2016)
      With the rapid development of mobile devices, spatial location-based crowdsourcing applications have attracted much attention. These applications also introduce new security risks due to untrustworthy data sources. In the ...
    • Ensemble classifier based approach for code-mixed cross-script question classification 

      Bhattacharjee, Debjyoti; Bhattacharya, Paheli (2016)
      With an increasing popularity of social-media, people post updates that aid other users in finding answers to their questions. Most of the user-generated data on social-media are in code-mixed or multi-script form, where ...
    • Wave forecasting using meta-cognitive interval type-2 fuzzy inference system 

      Anh, Nguyen; Prasad, Mukesh; Srikanth, Narasimalu; Sundaram, Suresh (2018)
      Renewable energy is fast becoming a mainstay in today’s energy scenario. One of the important sources of renewable energy is the wave energy, in addition to wind, solar, tidal, etc. Wave prediction/forecasting is consequently ...
    • A fast and self-adaptive on-line learning detection system 

      Prasad, Mukesh; Zheng, Ding-Rong; Mery, Domingo; Puthal, Deepak; Sundaram, Suresh; Lin, Chin-Teng (2018)
      This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection ...
    • Online video streaming for human tracking based on weighted resampling particle filter 

      Prasad, Mukesh; Chang, Liang-Cheng; Gupta, Deepak; Pratama, Mahardhika; Sundaram, Suresh; Lin, Chin-Teng (2018)
      This paper proposes a weighted resampling method for particle filter which is applied for human tracking on active camera. The proposed system consists of three major parts which are human detection, human tracking, and ...
    • Wind speed intervals prediction using meta-cognitive approach 

      Anh, Nguyen; Prasad, Mukesh; Srikanth, Narasimalu; Sundaram, Suresh (2018)
      In this paper, an interval type-2 neural fuzzy inference system and its meta-cognitive learning algorithm for wind speed prediction is proposed. Interval type-2 neuro-fuzzy system is capable of handling uncertainty associated ...
    • Autonomous agents in snake game via deep reinforcement learning 

      Wei, Zhepei; Wang, Di; Zhang, Ming; Tan, Ah-Hwee; Miao, Chunyan; Zhou, You (2018)
      Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, ...