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Related Experiment Video

Updated: Jan 4, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

Two efficient calculations of edit distance between marked point processes.

Yoshito Hirata¹, Noriyoshi Sukegawa²

¹Mathematics and Informatics Center and International Research Center for Neurointelligence, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Chaos (Woodbury, N.Y.)

|November 3, 2019

Summary

We introduce faster methods for calculating edit distances in marked point processes. By reframing the problem as a graph matching task, we achieve significant speedups for this complex data analysis.

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Last Updated: Jan 4, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

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Published on: April 9, 2019

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Area of Science:

Computational Mathematics
Data Science
Signal Processing

Background:

Calculating edit distances for marked point processes is computationally intensive.
Existing methods face challenges in efficiency for complex datasets.

Purpose of the Study:

To develop and evaluate faster algorithms for exact edit distance calculation in marked point processes.
To leverage graph theory and optimization techniques for improved computational performance.

Main Methods:

Application of linear programming methods.
Utilization of the Hungarian method, a specialized assignment algorithm.
Modeling the edit distance calculation as a bipartite graph matching problem.

Main Results:

Proposed methods demonstrate substantial speed improvements, ranging from 10x to 1000x faster than conventional approaches.
Preliminary numerical results validate the efficiency gains.

Conclusions:

Linear programming and the Hungarian method offer a significant advancement in calculating edit distances for marked point processes.
The reduction to a matching problem provides a computationally efficient framework for this task.