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Filtering by: Committee Member Fern, Xiaoli Remove constraint Committee Member: Fern, Xiaoli Subject Computer vision Remove constraint Subject: Computer vision

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  • Fine-grained detection and localization of objects in images

    Creator:
    Feng, Yaofei
    Abstract:
    Object recognition is a fundamental problem in computer vision. Recognition is required by many applications. This thesis presents a distance based approach to recognize objects. We are interested in objects that belong to very similar classes, where each class has large variations. This problem is called fine-grained object recognition. Given...
    Resource Type:
    Masters Thesis
    Full Text:
    . Fern for giving me precious guidance in machine learning research area. Last, thanks to my parents and

  • Computer vision for geometrical analysis of bridge gusset plates

    Creator:
    Varakantham, Shravya
    Abstract:
    Gusset plates are an important component of bridges. They are thick sheets of steel that join steel members together using fasteners and also strengthen their joint. Transportation agencies regularly evaluate and rate their inventories of gusset plate connections using visual inspection, which is very costly. To address this issue, we...
    Resource Type:
    Masters Thesis

  • Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

    Creator:
    Wang, Xinyao
    Abstract:
    Heatmap regression has became one of the mainstream approaches to localize facial landmarks. As Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are becoming popular in solving computer vision tasks, extensive research has been done on these architectures. However, the loss function for heatmap regression is rarely studied. In...
    Resource Type:
    Masters Thesis
    Full Text:
    committee members. Professor Xiaoli Fern introduced the world of machine learning to me. My research on

  • 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:
    other thesis committee members, Professor Fuxin Li, Professor Raviv Raich and Professor Xiaoli Fern. I

  • A study of methods for fine-grained object classification of arthropod specimens

    Creator:
    Lin, Junyuan
    Abstract:
    Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...
    Resource Type:
    Masters Thesis