ArticleInverse problems and imaging (Springfield, Mo.)2023
MULTI-TARGET DETECTION WITH ROTATIONS.
Article in Inverse problems and imaging (Springfield, Mo.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- EXPECTATION-MAXIMIZATION FOR STRUCTURE DETERMINATION DIRECTLY FROM CRYO-EM MICROGRAPHS.Inverse problems and imaging (Springfield, Mo.) · 2027Article
- Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms.Proceedings of the National Academy of Sciences of the United States of America · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We consider the multi-target detection problem of estimating a two-dimensional target image from a large noisy measurement image that contains many randomly rotated and translated copies of the target image. Motivated by single-particle cryo-electron microscopy, we focus on the low signal-to-noise regime, where it is difficult to estimate the locations and orientations of the target images in the measurement. Our approach uses autocorrelation analysis to estimate rotationally and translationally invariant features of the target image. We demonstrate that, regardless of the level of noise, our technique can be used to recover the target image when the measurement is sufficiently large.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.