TY - GEN
T1 - Shape measurement system of foot sole surface from flatbed scanner image
AU - Martedi, Sandy
AU - Saito, Hideo
AU - Servières, Myriam
PY - 2009/12/1
Y1 - 2009/12/1
N2 - We present a system to measure 3D shape of sole surface of human foot using flatbed scanner. Our goal is to build a low cost system for reconstructing sole surface of human foot using flatbed scanner for making tailored shoes. There are two main phases in our system: a photometric parameter estimation and a reconstruction phase. The photometric parameter estimation calculates the position of the light source in the scanner. The reconstruction phase uses iterative algorithm to calculate normal vector and the depth information. First, we implement photometric parameter estimation by scanning some white paper in several slant and rotation angles. We conducted some experiments to obtain the best slant and rotation angle combination for calculating light source position of the scanner. We also propose a new method for estimating light source position of the scanner using foot model. Next, we conduct reconstruction phase by scanning user's foot. We then apply median filter with 5×5 mask sizes to remove the noise in scanned image. By using the calculated light source position from the previous step and pixel intensity of scanned image, depth and normal vector are calculated iteratively. We acquire more accurate light source position by comparing albedo ratio and finally we acquire the 3D shape which average error compared with ground truth data is up to 0.97mm.
AB - We present a system to measure 3D shape of sole surface of human foot using flatbed scanner. Our goal is to build a low cost system for reconstructing sole surface of human foot using flatbed scanner for making tailored shoes. There are two main phases in our system: a photometric parameter estimation and a reconstruction phase. The photometric parameter estimation calculates the position of the light source in the scanner. The reconstruction phase uses iterative algorithm to calculate normal vector and the depth information. First, we implement photometric parameter estimation by scanning some white paper in several slant and rotation angles. We conducted some experiments to obtain the best slant and rotation angle combination for calculating light source position of the scanner. We also propose a new method for estimating light source position of the scanner using foot model. Next, we conduct reconstruction phase by scanning user's foot. We then apply median filter with 5×5 mask sizes to remove the noise in scanned image. By using the calculated light source position from the previous step and pixel intensity of scanned image, depth and normal vector are calculated iteratively. We acquire more accurate light source position by comparing albedo ratio and finally we acquire the 3D shape which average error compared with ground truth data is up to 0.97mm.
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M3 - Conference contribution
AN - SCOPUS:84872698966
SN - 9784901122092
T3 - Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
SP - 338
EP - 341
BT - Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
T2 - 11th IAPR Conference on Machine Vision Applications, MVA 2009
Y2 - 20 May 2009 through 22 May 2009
ER -