Cell AT-models for digital volumes
In [4], given a binary 26-adjacency voxel-based digital volume V, the homological information (that related to n-dimensional holes: connected components, ”tunnels” and cavities) is extracted from a linear map (called homology gradient vector field) acting on a polyhedral cell complex P(V) homologica...
| Authors: | , |
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| Format: | book part |
| Status: | Versión enviada para evaluación y publicación |
| Publication Date: | 2009 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/31749 |
| Online Access: | http://hdl.handle.net/11441/31749 https://doi.org/10.1007/978-3-642-02124-4_32 |
| Access Level: | Open access |
| Keyword: | Pattern Recognition Image Processing and Computer Vision Computer Imaging Vision Pattern Recognition and Graphics Computer Graphics Discrete Mathematics in Computer Science Artificial Intelligence (incl. Robotics) |
| Summary: | In [4], given a binary 26-adjacency voxel-based digital volume V, the homological information (that related to n-dimensional holes: connected components, ”tunnels” and cavities) is extracted from a linear map (called homology gradient vector field) acting on a polyhedral cell complex P(V) homologically equivalent to V. We develop here an alternative way for constructing P(V) based on homological algebra arguments as well as a new more efficient algorithm for computing a homology gradient vector field based on the contractibility of the maximal cells of P(V). |
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