PT Journal
AU Joan Serrat
   Felipe Lumbreras
   Idoia Ruiz
TI Learning to measure for preshipment garment sizing
SO Measurement
JI MEASURE
PY 2018
BP 327
EP 339
VL 130
DE Apparel; Computer vision; Structured prediction; Regression
AB 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.
ER