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Filtering by: Committee Member Todorovic, Sinisa Remove constraint Committee Member: Todorovic, Sinisa Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Degree Level Doctoral Remove constraint Degree Level: Doctoral

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  • 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:
    to each member of my committee: Prof. Sinisa Todorovic, Prof. Raviv Raich, Prof. Prasad Tadepalli

  • Learning with Partial Supervision for Clustering and Classification

    Creator:
    Pei, Yuanli
    Abstract:
    In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
    Resource Type:
    Dissertation
    Full Text:
    meetings and serving on my committee. I thank the rest of my committee members: Prasad Tadepalli, Sinisa

  • Object Tracking-by-Segmentation in Videos

    Creator:
    Chen, Sheng
    Abstract:
    This thesis focuses on the problem of object tracking. Given a video, the general objective of tracking is to track the location over time of one or more targets in the image sequence. This is a very challenging task as algorithms need to deal with problems such as appearance variations,...
    Resource Type:
    Dissertation
    Full Text:
    also like to thank Prof Todorovic. I always treat you as my co-advisor and I’m very thankful to all

  • Toward computer vision for understanding American football in video

    Creator:
    Hess, Robin W.
    Abstract:
    In this work, I examine the problem of understanding American football in video. In particular, I present several mid-level computer vision algorithms that each accomplish a different sub-task within a larger system for annotating, interpreting, and analyzing collections of American football video. The analysis of football video is useful in...
    Resource Type:
    Dissertation
    Full Text:
    . Ron Metoyer, Dr. Sinisa Todorovic, and Dr. Eric Mortensen. Through working with each of you in