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Filtering by: Creator Su, Yi-Hsuan Remove constraint Creator: Su, Yi-Hsuan Committee Member Li, Fuxin Remove constraint Committee Member: Li, Fuxin Subject Machine learning Remove constraint Subject: Machine learning

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  • Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

    Creator:
    Lei, Peng
    Abstract:
    This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
    Resource Type:
    Dissertation
    Full Text:
    : Tianfu Wu, Zhenyu Yao, Jiangen Zhang, Xiaohan Nie, Bo Li, Xi Song and Yi Xie. Last but not least, I

  • Robust and Efficient Classification of Videos in the Wild

    Creator:
    Mahasseni, Behrooz
    Abstract:
    Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
    Resource Type:
    Dissertation
    Full Text:
    use the same feature space, with feature vectors x. Given training data Dv = {(xi, yi) : i = 1, 2

  • Lamb Wave Mode Decomposition and Its Applications in Structural Health Monitoring

    Creator:
    Zoubi, Ahmad Bassil
    Abstract:
    Structural health monitoring (SHM) systems perform automated non-destructive damage detection and characterization for a variety of large structures including civil structures such as bridges and aerospace structures such as aircrafts and space vehicles. The goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing maintenance costs, and increasing the...
    Resource Type:
    Dissertation