Name
Abstract | ||
|---|---|---|
The automatic detection of different foot’s diseases requires the analysis of a footprint, obtained from a digital image of the sole. This paper shows that optical monochromatic images are not suitable for footprint segmentation purposes, while color images provide enough information for carrying out an efficient segmentation. It is shown that a multiplayer perceptron trained with bayesian regularization backpropagation allows to adequately classify the pixels on the color image of the footprint and in this way, to segment the footprint without fingers. The footprint is improved by using a classical smoothing filter, and segmented by performing erosion and dilation operations. This result is very important for the development of a low cost system designed to diagnose pathologies related to the footprint form. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11578079_33 | CIARP |
Keywords | Field | DocType |
different foot,automatic detection,neural network,classical smoothing filter,digital image,color image,optical monochromatic image,footprint form,bayesian regularization backpropagation,footprint segmentation purpose,efficient segmentation,robust footprint detection,backpropagation,system design,bayesian regularization | Computer vision,Pattern recognition,Computer science,Segmentation,Image processing,Digital image,Image segmentation,Smoothing,Footprint,Pixel,Artificial intelligence,Color image | Conference |
Volume | ISSN | ISBN |
3773 | 0302-9743 | 3-540-29850-9 |
Citations | PageRank | References |
5 | 0.57 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Mora | 1 | 32 | 9.08 |
| Daniel Sbarbaro | 2 | 49 | 12.84 |