Reconstructing persistent graph structures from noisy images

Alexey Chernov; Vitaliy Kurlin

Reconstructing persistent graph structures from noisy images

University of Brighton

Research output: Contribution to journal › Article › peer-review

Abstract

Let a point cloud be a noisy dotted image of a graph on the plane. We present a new fast algorithm for reconstructing the original graph from the given point cloud. Degrees of vertices in the graph are found by methods of persistent topology. Necessary parameters are automatically optimized by machine learning tools.

Original language	English
Pages (from-to)	19-22
Number of pages	4
Journal	Image-A: Applicable Mathematics in Image Engineering
Volume	3
Issue number	5
Publication status	Published - 15 Mar 2013

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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 International License

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title = "Reconstructing persistent graph structures from noisy images",

abstract = "Let a point cloud be a noisy dotted image of a graph on the plane. We present a new fast algorithm for reconstructing the original graph from the given point cloud. Degrees of vertices in the graph are found by methods of persistent topology. Necessary parameters are automatically optimized by machine learning tools.",

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Reconstructing persistent graph structures from noisy images

Abstract

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