# KLT Picker: Particle Picking Using Data-Driven Optimal Templates

arXiv: https://arxiv.org/abs/1912.06500 · HTML: https://mlsanity.com/paper/1912.06500/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Multi-target detection with rotations](https://mlsanity.com/paper/2101.07709/) (2021)
- [ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM](https://mlsanity.com/paper/2201.06978/) (2022)
- [Reconstruction-Aware Cryo-EM Particle Picking](https://mlsanity.com/paper/2608.28838/) (2026)

## Abstract (arXiv)

Particle picking is currently a critical step in the cryo-EM single particle reconstruction pipeline. Despite extensive work on this problem, for many data sets it is still challenging, especially for low SNR micrographs. We present the KLT (Karhunen Loeve Transform) picker, which is fully automatic and requires as an input only the approximated particle size. In particular, it does not require any manual picking. Our method is designed especially to handle low SNR micrographs. It is based on learning a set of optimal templates through the use of multi-variate statistical analysis via the Karhunen Loeve Transform. We evaluate the KLT picker on publicly available data sets and present high-quality results with minimal manual effort.