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Found 4 papers, 1 papers with code
Image Segmentation via Probabilistic Graph Matching
This work presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as a inference problem based on unary and pairwise assignment probabilities computed using low-level image cues.
Bias and variance reduction and denoising for CTF Estimation
Our method is based on the multi-taper method for power spectral density estimation, which aims to reduce the bias and variance of the estimator.
Semi-supervised Learning in Network-Structured Data via Total Variation Minimization
This lends naturally to learning the labels by total variation (TV) minimization, which we solve by applying a recently proposed primal-dual method for non-smooth convex optimization.
APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework
Selecting particles from the micrographs is difficult especially for small particles with low contrast.
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