Abstract
An algorithm for partitioning the shape of a planar pattern into near-convex parts is described, which utilizes ancillary processes such as dominant point detection and label propagation. The shape features taken into account are negative minima of curvature on the contour and spines, i.e. suitable skeleton subsets. Each spine has its own view of the pattern and exerts an influence over the neighboring pixels as far as no conflicts with other spines arise. The parts of the decomposition are found as the zones of influence of the spines. To favor a more compact description, the algorithm also includes a phase devoted to the extraction of loop components in correspondence with the holes of the pattern.
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