ArticleIEEE signal processing letters2022
An approximate expectation-maximization for two-dimensional multi-target detection.
Article in IEEE signal processing letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- EXPECTATION-MAXIMIZATION FOR STRUCTURE DETERMINATION DIRECTLY FROM CRYO-EM MICROGRAPHS.Inverse problems and imaging (Springfield, Mo.) · 2027Article
- Multi-target detection and tracking based on CRF network and spatio-temporal attention for sports videos.Scientific reports · 2025Article
- MULTI-TARGET DETECTION WITH ROTATIONS.Inverse problems and imaging (Springfield, Mo.) · 2023Article
- Toward Single Particle Reconstruction without Particle Picking: Breaking the Detection Limit.SIAM journal on imaging sciences · 2023Article
- An approximate expectation-maximization for two-dimensional multi-target detection.IEEE signal processing letters · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
We consider the two-dimensional multi-target detection (MTD) problem of estimating a target image from a noisy measurement that contains multiple copies of the image, each randomly rotated and translated. The MTD model serves as a mathematical abstraction of the structure reconstruction problem in single-particle cryo-electron microscopy, the chief motivation of this study. We focus on high noise regimes, where accurate detection of image occurrences within a measurement is impossible. To estimate the image, we develop an expectation-maximization framework that aims to maximize an approximation of the likelihood function. We demonstrate image recovery in highly noisy environments, and show that our framework outperforms the previously studied autocorrelation analysis in a wide range of parameters.
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