# Multi-target detection with rotations

arXiv: https://arxiv.org/abs/2101.07709 · HTML: https://mlsanity.com/paper/2101.07709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM](https://mlsanity.com/paper/1605.01838/) (2016)
- [Estimation under group actions: recovering orbits from invariants](https://mlsanity.com/paper/1712.10163/) (2017)
- [Method of moments for 3-D single particle ab initio modeling with non-uniform distribution of viewing angles](https://mlsanity.com/paper/1907.05377/) (2019)
- [APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework](https://mlsanity.com/paper/1802.00469/) (2018)
- [Fundamental Limits in Multi-image Alignment](https://mlsanity.com/paper/1602.01541/) (2016)
- [Multi-reference alignment in high dimensions: sample complexity and phase transition](https://mlsanity.com/paper/2007.11482/) (2020)
- [3D ab initio modeling in cryo-EM by autocorrelation analysis](https://mlsanity.com/paper/1710.08076/) (2017)
- [Image recovery from rotational and translational invariants](https://mlsanity.com/paper/1910.10006/) (2019)
- [KLT Picker: Particle Picking Using Data-Driven Optimal Templates](https://mlsanity.com/paper/1912.06500/) (2019)
- [Multi-target Detection with an Arbitrary Spacing Distribution](https://mlsanity.com/paper/1905.03176/) (2019)
- [Super-resolution multi-reference alignment](https://mlsanity.com/paper/2006.15354/) (2020)

## Built on by (most cited first, 1 of 1)

- [Score-based diffusion priors for multi-target detection](https://mlsanity.com/paper/2312.08500/) (2023)

## Abstract (arXiv)

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.