Abstract
We present a new method of segmentation in which images are segmented by partitions with connected components. For this, first we define two different types of neighborhoods on the space of partitions with connected components of a general graph, neighborhoods of the first type are simple but small, while those of the second type are large but complex; second, we give algorithms which are not computationally costly, for probability simulation and simulated annealing on such spaces using the neighborhoods. In particular Hastings algorithms and generalized Metropolis algorithms are defined to avoid heavy computations in the case of the second type of neighborhoods. To realize segmentation, we propose a hierarchical approach which at each step, minimizes a cost function on the space of partitions with connected components of a graph.
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© 1997 Springer-Verlag Berlin Heidelberg
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Wang, JP. (1997). Image segmentation via energy minimization on partitions with connected components. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_75
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DOI: https://doi.org/10.1007/3-540-62909-2_75
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