Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Deep Learning Approach to Structure From Polarization

Alazemi, Ahmad HMH
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1627820674064972

Abstract Details

2021, Master of Science in Electrical Engineering, University of Dayton, Electrical and Computer Engineering.
Imaging polarimeters are a type of imaging device that attempts to estimate the polarized Stokes vector at each point in an imaged scene. Polarization has shown the ability to reduce background clutter, defeat atmospheric scatterers, improve scene contrast within polarized regions, and provide shape information about polarized objects of interest. Measured angle of polarization imagery tends to be highly correlated with the azimuthal component of object surface normal vectors. Hence, while polarimetric images do not directly provide 3D scene information, our goal in this work is to investigate the applicability of deep learning approaches for estimation of 3D structure from polarimetric image data. Unlike other image modalities, no repositories of polarimetric training data are readily available for training and testing purposes. As part of this work, we design a set of laboratory-based data collection experiments under a controlled set of scene conditions to obtain a sufficient set of polarimetric training and testing data. We then develop a deep learning approach to structure from polarization based upon the Pix2Pix conditional generative adversarial network for image translation problems. Initial results from training and testing our approach are presented that demonstrate promise for obtaining pixel-wise 3D information from polarimetric image data.
Bradley Ratliff, Ph.D. (Committee Chair)
Amy Doll, Ph.D. (Committee Member)
Barath Narayanan, Ph.D. (Committee Member)
43 p.
Computer Engineering; Electrical Engineering; Engineering; Optics
Deep learning; polarization; 3D structure; Degree of linear polarization; Angle of polarization

Recommended Citations

Citations

  • Alazemi, A. H. (2021). Deep Learning Approach to Structure From Polarization [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1627820674064972

    APA Style (7th edition)

  • Alazemi, Ahmad. Deep Learning Approach to Structure From Polarization. 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1627820674064972.

    MLA Style (8th edition)

  • Alazemi, Ahmad. "Deep Learning Approach to Structure From Polarization." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1627820674064972

    Chicago Manual of Style (17th edition)

dayton1627820674064972
372
© 2021, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.