Abstract
This paper1 presents an application of several definitions of a mean set for use in footwear design. For a given size, footprint pressure images corresponding to different individuals constitute our raw data. Appropriate footwear design needs to have knowledge of some kind of typical footprint. Former methods based on contour relevant points are highly sensitive to contour noise; moreover, they lack repeatability because of the need for the intervention of human designers. The method proposed in this paper is based on using mean sets on the thresholded images of the pressure footprints. Three definitions are used, two of them from Vorob’ev and Baddeley-Molchanov and one morphological mean proposed by the authors. Results show that the use of mean sets improves previous methodologies in terms of robustness and repeatability.
This work has been supported by projects TIC2002-03494 from the Spanish Ministry of Science and Technology, CTIDIA-2002-133, RGY 40/2003 and GV04B-032 and GV04A-177 from the Generalitat Valenciana.
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Domingo, J. et al. (2005). Quantifying Mean Shape and Variability of Footprints Using Mean Sets. In: Ronse, C., Najman, L., Decencière, E. (eds) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3443-1_41
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DOI: https://doi.org/10.1007/1-4020-3443-1_41
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