skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Zehni, Mona"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ( , SIAM Journal on Imaging Sciences)
    Free, publicly-accessible full text available September 30, 2024
  2. ; ( , International Conference on Acoustics, Speech, and Signal Processing (ICASSP))
    Free, publicly-accessible full text available June 4, 2024
  3. ; ( , IEEE Transactions on Computational Imaging)
    Full Text Available 
  4. ; ( , 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP))
    null (Ed.)
    Full Text Available 
  5. ; ( , 2021 IEEE 18th International Symposium on Biomedical Imaging)
    null (Ed.)
    Full Text Available 
  6. ; ; ; ; ; ; ( , SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval)
    null (Ed.)
    Full Text Available 
  7. ; ; ; ( , IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
    null (Ed.)
    Full Text Available 
  8. ; ; ; ; ( , IEEE Transactions on Image Processing)
    Full Text Available 
  9. ; ; ; ( , ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
    In this paper, we study a 2D tomography problem for point source models with random unknown view angles. Rather than recovering the projection angles, we reconstruct the model through a set of rotation-invariant features that are estimated from the projection data. For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances. This establishes a connection between unknown view tomography and unassigned distance geometry problem (uDGP). We propose new methods to extract the distances and approximate the pairwise distance distribution of the underlying points. We then use the recovered distribution to estimate the locations of the points through constrained non-convex optimization. Our simulation results show that our point source reconstruction pipeline is robust to noise and outperforms the regularized expectation maximization (EM) baseline. 
    more » « less
    Full Text Available