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Polarimetric Imagery for Object Pose Estimation

Siefring, Matthew D
http://rave.ohiolink.edu/etdc/view?acc_num=dayton168233362995024

Abstract Details

2023, Master of Science (M.S.), University of Dayton, Electrical Engineering.
Polarization imaging is a rich modality that describes the orientation of reflected optical radiance in a scene. Polarization has been shown to be useful for computer vision tasks by improving robustness to low visibility conditions, improving contrast between polarized and non-polarized objects, and providing shape information about polarized objects. However, properly applying polarimetric information to convolutional neural networks (CNNs) is an ongoing area of research. In this work, our goal is to explore new and existing methods of introducing polarimetric imagery to pretrained RGB intensity CNNs for the purpose of object pose estimation. As part of our research, we design and execute a controlled data collection where we measure the linear Stokes parameters at each point in a well-lit image. For each well-lit image, we generate a synthetic low-light image. We then develop a pipeline to generate 3D bounding box parameters for objects of interest in a semi-automated manner. Lastly, we use our dataset to create several deep-learning-based pose estimation models which utilize polarization information in differing ways. We compare the pose estimation performance of each network under varying illumination conditions.
Bradley Ratliff (Committee Chair)
Jason Kaufman (Committee Member)
Eric Balster (Committee Member)
63 p.
Electrical Engineering; Optics
Polarimetric Imagery; visible-spectrum; deep-learning; object pose estimation; CNN; late-fusion; Stokes-products dataset

Recommended Citations

Citations

  • Siefring, M. D. (2023). Polarimetric Imagery for Object Pose Estimation [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton168233362995024

    APA Style (7th edition)

  • Siefring, Matthew. Polarimetric Imagery for Object Pose Estimation. 2023. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton168233362995024.

    MLA Style (8th edition)

  • Siefring, Matthew. "Polarimetric Imagery for Object Pose Estimation." Master's thesis, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton168233362995024

    Chicago Manual of Style (17th edition)

dayton168233362995024
61
© 2023, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.