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Organizers: Joakim Andén, Dorit Hanein, Roy R. Lederman and Steven J. Ludtke

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Fast Normalized Cross-Correlation for Template Matching with Rotations

Object detection is a main task in computer vision. Template matching is the reference method for detecting objects with arbitrary templates. However, template matching computational complexity depends on the rotation accuracy, being a limiting factor for large 3D images (tomograms). 

Here, we implement a new algorithm called tensorial template matching, based on a mathematical framework that represents all rotations of a template with a tensor field. Contrary to standard template matching, the computational complexity of the presented algorithm is independent of the rotation accuracy.

Using both, synthetic and real data from cryo-electron tomography, we demonstrate that tensorial template matching is much faster than template matching and has the potential to improve its accuracy.

Last Talk

Aaditya Rangan
Associate Professor, Courant Institute, New York University and Flatiron Institute.

Date and time: May 8th, 2024 Wednesday, at 8am PDT / 11am EDT / 3pm BST / 5pm CEST / 11pm China

Ab-initio Reconstruction from small datasets: using entropy-maximization and principal modes (EMPM).

In this talk I’ll describe a simple algorithm for obtaining a low-resolution model from a small number (~1000) picked-particle images. The algorithm (termed EMPM) is similar to classical alternating-minimization, but with a slight twist: the entropy of the viewing-angle distribution is maximized during the search. This entropy-maximization greatly improves robustness and accuracy, allowing EMPM to outperform many existing ab-initio reconstruction algorithms. Because of its low computational overhead, this algorithm could facilitate statistical strategies such as bootstrapping and cross-validation, along with the rapid assessment of particle quality.