Abstract
The eccentricity transform associates to each point of a shape the shortest distance to the point farthest away from it. It is defined in any dimension, for open and closed manyfolds. Top-down decomposition of the shape can be used to speed up the computation, with some partitions being better suited than others. We study basic convex shapes and their decomposition in the context of the continuous eccentricity transform. We show that these shapes can be decomposed for a more efficient computation. In particular, we provide a study regarding possible decompositions and their properties for the ellipse, the rectangle, and a class of elongated shapes.
Supported by the Austrian Science Fund under grants P18716-N13 and S9103-N04.
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Ion, A., Peltier, S., Haxhimusa, Y., Kropatsch, W.G. (2007). Decomposition for Efficient Eccentricity Transform of Convex Shapes. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_81
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DOI: https://doi.org/10.1007/978-3-540-74272-2_81
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