<abstract xmlns="http://www.w3.org/1999/xhtml"><p> This work extends the dynamic programming approach to calculation of an elastic metric between two curves to finding paths in pairs of graph drawings that are closest under this metric. The new algorithm effectively solves this problem when all paths between two given nodes in one of these graphs have the same length. It is then applied to the problem of pattern recognition constrained by a superpixel segmentation. Segmentations of test images, obtained without statistical modeling given two shape endpoints, have good accuracy.</p></abstract>

This work extends the dynamic programming approach to calculation of an elastic metric between two curves to finding paths in pairs of graph drawings that are closest under this metric. The new algorithm effectively solves this problem when all paths between two given nodes in one of these graphs have the same length. It is then applied to the problem of pattern recognition constrained by a superpixel segmentation. Segmentations of test images, obtained without statistical modeling given two shape endpoints, have good accuracy.

Closest Paths in Graph Drawings under an Elastic Metric

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Closest Paths in Graph Drawings under an Elastic Metric"}

This work extends the dynamic programming approach to calculation of an elastic metric between two curves to finding paths in pairs of graph drawings that are...

Closest Paths in Graph Drawings under an Elastic Metric

Online veröffentlicht: 29. Juni 2018

Seitenbereich: 387 - 397

Eingereicht: 15. Mai 2017

Akzeptiert: 19. Nov. 2017

DOI: https://doi.org/10.2478/amcs-2018-0029

Schlüsselwörter
elastic shape analysis, pattern recognition, superpixel segmentation

© 2018 Mateusz Baran, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Closest Paths in Graph Drawings under an Elastic Metric

Online veröffentlicht: 29. Juni 2018

Seitenbereich: 387 - 397

Eingereicht: 15. Mai 2017

Akzeptiert: 19. Nov. 2017

DOI: https://doi.org/10.2478/amcs-2018-0029

Schlüsselwörterelastic shape analysis, pattern recognition, superpixel segmentation

© 2018 Mateusz Baran, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
elastic shape analysis, pattern recognition, superpixel segmentation