Name
Abstract | ||
|---|---|---|
This paper(1) 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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/1-4020-3443-1_41 | COMPUTATIONAL IMAGING AND VISION |
Keywords | Field | DocType |
mean set,morphological inean,footprint,footwear design | Pattern recognition,Computer science,Raw data,Robustness (computer science),Real-time computing,Artificial intelligence,Footprint,Repeatability | Conference |
Volume | Citations | PageRank |
30 | 1 | 0.36 |
References | Authors | |
3 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Domingo | 1 | 3319 | 258.54 |
| B. Nacher | 2 | 1 | 0.36 |
| E. De Ves | 3 | 119 | 7.62 |
| E. Alcantara | 4 | 1 | 0.36 |
| E. Diaz | 5 | 1 | 0.36 |
| G. Ayala | 6 | 16 | 3.55 |
| Álvaro Page | 7 | 7 | 5.17 |