TY  - JOUR
AU  - Joan Serrat
AU  - Felipe Lumbreras
AU  - Idoia Ruiz
PY  - 2018//
TI  - Learning to measure for preshipment garment sizing
T2  - MEASURE
JO  - Measurement
SP  - 327
EP  - 339
VL  - 130
KW  - Apparel
KW  - Computer vision
KW  - Structured prediction
KW  - Regression
N2  - Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.
UR  - https://doi.org/10.1016/j.measurement.2018.08.019
L1  - http://refbase.cvc.uab.es/files/SLR2018.pdf
N1  - ADAS; MSIAU; 600.122; 600.118
ID  - Joan Serrat2018
ER  -