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Filtering by: Creator Cirpka, Olaf A. Remove constraint Creator: Cirpka, Olaf A. Advisor Li, Fuxin Remove constraint Advisor: Li, Fuxin

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  • Deep Perception Without a Camera: Enabling 3D Reconstruction and Object Recognition using Lidar and Sonar Sensing

    Creator:
    DeBortoli, Robert A.
    Abstract:
    Deep learning has recently revolutionized robot perception in many canonical robotic applications, such as autonomous driving. However, a similar transformation has yet to occur in more harsh environments including underwater and underground. This is due in part to the difficulty in deploying robots in these environments, which lack large real...
    Resource Type:
    Dissertation
    Full Text:
    Without a Camera: Enabling 3D Reconstruction and Object Recognition using Lidar and Sonar Sensing

  • Deep Segmentation in Plant Phenomics

    Creator:
    Kaur, Damanpreet
    Abstract:
    The abilities of plant biologists to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation and mainly to collect this data on a high-throughput scale at low cost. Deep learning-based methods have demonstrated unprecedented potential to automate...
    Resource Type:
    Masters Thesis
    Full Text:
    representing precise details of trait variation and mainly to collect this data on a high-throughput scale at

  • Visual object discovery and understanding

    Creator:
    Yuan, Jialin
    Abstract:
    Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
    Resource Type:
    Dissertation
    Full Text:
    a fundamental and essential step in human perception and understanding of the world. Accordingly

  • Leveraging Structures of the Data in Deep Learning

    Creator:
    Li, Xingyi
    Abstract:
    The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
    Resource Type:
    Dissertation
    Full Text:
    justify that, for convolutional neural networks (CNNs), neighborhoods of a pixel should be redefined as

  • Deep Convolutional Network on Point Clouds for 3D Scene Understanding

    Creator:
    Wu, Wenxuan
    Abstract:
    As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in...
    Resource Type:
    Dissertation
    Full Text:
    in those applications as depth information could remove a lot of the segmentation ambiguities in 2D