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Measuring the distance of generalized maps

Camille Combier 1 Christine Solnon 1 Guillaume Damiand 2, 1
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 SIC
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Generalized maps are widely used to model the topology of nD objects (such as images) by means of incidence and adjacency relationships between cells (vertices, edges, faces, volumes, ...). In this paper, we define a first error-tolerant distance measure for comparing generalized maps, which is an important issue for image processing and analysis. This distance measure is defined by means of the size of a largest common submap, in a similar way as a graph distance measure may be defined by means of the size of a largest common subgraph. We show that this distance measure is a metric, and we introduce a greedy randomized algorithm which allows us to efficiently compute an upper bound of it.
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Submitted on : Monday, May 23, 2011 - 3:42:28 PM
Last modification on : Thursday, November 21, 2019 - 2:13:57 AM
Document(s) archivé(s) le : Saturday, December 3, 2016 - 12:27:53 PM

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Camille Combier, Christine Solnon, Guillaume Damiand. Measuring the distance of generalized maps. 8th IAPR - TC-15 Workshop on Graph-based Representations in Pattern Recognition, May 2011, Münster, Germany. pp.82-91, ⟨10.1007/978-3-642-20844-7⟩. ⟨hal-00595088⟩

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