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  • Adaptive model-based motion estimation

    Creator:
    Crinon, Regis
    Abstract:
    A general discrete-time, adaptive, multidimensional framework is introduced for estimating the motion of one or several object features from their successive non-linear projections on an image plane. The motion model consists of a set of linear difference equations with parameters estimated recursively from a non-linear observation equation. The model dimensionality...
    Resource Type:
    Dissertation
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    of solving specific computer vision problems. For the purpose of machine vision, the images at hand

  • 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
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    of Doctor of Philosophy in Computer Science presented on December 7, 2018. Title: Pixel- and Frame

  • 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...
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    Dissertation
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    and other markings, computer vision algorithms developed towards the end goal of understanding

  • From multitarget tracking to event recognition in videos

    Creator:
    Brendel, William
    Abstract:
    This dissertation addresses two fundamental problems in computer vision—namely, multitarget tracking and event recognition in videos. These problems are challenging because uncertainty may arise from a host of sources, including motion blur, occlusions, and dynamic cluttered backgrounds. We show that these challenges can be successfully addressed by using a multiscale,...
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    Dissertation
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    degree of Doctor of Philosophy in Computer Science presented on April 25, 2011. Title: From Multitarget

  • Automated Monitoring of Modular Construction Factories using Computer Vision

    Creator:
    Panahi, Roshan
    Abstract:
    Modular construction is increasingly seen as an efficient construction method in terms of time, cost, and energy. The full realization of these advantages partly relies on the efficiency of the production process inside the modular factories, which currently rely on tedious manual monitoring methods or expensive automated techniques. As a...
    Resource Type:
    Dissertation
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    . Title: Automated Monitoring of Modular Construction Factories using Computer Vision. Abstract

  • A Vision System for Automatic Dendrometry and Forest Mapping

    Creator:
    Wells, Lucas A.
    Abstract:
    In pursuit of global sustainability, forestry has witnessed significant shifts in practices and the development of new technologies and ideas. Primary and secondary processing industries have made substantial efforts to increase wood utilization rates, improve occupational safety and the working environment for humans, and have exhibited interest in procuring raw...
    Resource Type:
    Dissertation
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    Dissertation of Lucas A. Wells AN ABSTRACT OF THE

  • Hierarchical graphical models for activity recognition in videos

    Creator:
    Amer, Mohamed R.
    Abstract:
    This dissertation addresses the problem of recognizing human activities in videos. Our focus is on activities with stochastic structure, where the activities are characterized by variable space-time arrangements of actions, and conducted by a variable number of actors. These activities occur frequently in sports and surveillance videos. They may appear...
    Resource Type:
    Dissertation
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    empirical studies advance computer vision through demonstrated advantages of each paradigm, compared to the

  • Determination of human visual capabilities in the identification of the color of highway signs under a combination of vehicle headlamp and high intensity discharge light sources

    Creator:
    Saremi, Ahmad Reza
    Abstract:
    A standardized color code is used to aid the driver in the prompt recognition of highway signs. At night, these signs are illuminated by various light sources including the headlights and other fixed light sources. These light sources may distort the appearance of the colors of the signs at night....
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    Dissertation
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    tested to verify a threshold level of 20/40. Subjects were also given a color vision test to verify

  • Boundaries of research : civilian leadership, military funding, and the international network surrounding the development of numerical weather prediction in the United States

    Creator:
    Harper, Kristine C.
    Abstract:
    American meteorology was synonymous with subjective weather forecasting in the early twentieth century. Controlled by the Weather Bureau and with no academic programs of its own, the few hundred extant meteorologists had no standing in the scientific community. Until the American Meteorological Society was founded in 1919, meteorologists had no...
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    Dissertation
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    Funding, and the International Network Surrounding the Development of Numerical Weather Prediction in the

  • Image features and learning algorithms for biological, generic and social object recognition

    Creator:
    Zhang, Wei
    Abstract:
    Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require...
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    Dissertation
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    University of Washington. They are top researchers in computer vision. Their great expertise benefitted me