Abstract
Topological map is a mathematical model of labeled image representation which contains both topological and geometrical information. In this work, we use this model to improve a Markovian segmentation algorithm. Image segmentation methods based on Markovian assumption consist in optimizing a Gibbs energy function. This energy function can be given by a sum of potentials which could be based on the shape or the size of a region, the number of adjacencies,...and can be computed by using topological map. In this work we propose the integration of a new potential: the global linearity of the boundaries, and show how this potential can be extracted from the topological map. Moreover, to decrease the complexity of our algorithm, we propose a local modification of the topological map in order to avoid the reconstruction of the entire structure.
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Damiand, G., Alata, O., Bihoreau, C. (2003). Using 2D Topological Map Information in a Markovian Image Segmentation. In: Nyström, I., Sanniti di Baja, G., Svensson, S. (eds) Discrete Geometry for Computer Imagery. DGCI 2003. Lecture Notes in Computer Science, vol 2886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39966-7_27
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DOI: https://doi.org/10.1007/978-3-540-39966-7_27
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