Topological tracking of connected components in image sequences
Persistent homology provides information about the lifetime of homology classes along a filtration of cell complexes. Persistence barcode is a graphi- cal representation of such information. A filtration might be determined by time in a set of spatiotemporal data, but classical methods for computing...
| Authors: | , , |
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| Format: | article |
| Status: | Versión enviada para evaluación y publicación |
| Publication Date: | 2018 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/83553 |
| Online Access: | https://hdl.handle.net/11441/83553 https://doi.org/10.1016/j.jcss.2017.12.005 |
| Access Level: | Open access |
| Keyword: | Persistent homology Persistence barcodes Spatiotemporal data Binary digital image sequence analysis |
| Summary: | Persistent homology provides information about the lifetime of homology classes along a filtration of cell complexes. Persistence barcode is a graphi- cal representation of such information. A filtration might be determined by time in a set of spatiotemporal data, but classical methods for computing persistent homology do not respect the fact that we can not move back- wards in time. In this paper, taking as input a time-varying sequence of two-dimensional (2D) binary digital images, we develop an algorithm for en- coding, in the so-called spatiotemporal barcode, lifetime of connected compo- nents (of either the foreground or background) that are moving in the image sequence over time (this information may not coincide with the one provided by the persistence barcode). This way, given a connected component at a specific time in the sequence, we can track the component backwards in time until the moment it was born, by what we call a spatiotemporal path. The main contribution of this paper with respect to our previous works lies in a new algorithm that computes spatiotemporal paths directly, valid for both foreground and background and developed in a general context, setting the ground for a future extension for tracking higher dimensional topological features in nD binary digital image sequences. |
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