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Author (up) Joan Serrat; Felipe Lumbreras; Idoia Ruiz edit  url
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  Title Learning to measure for preshipment garment sizing Type Journal Article
  Year 2018 Publication Measurement Abbreviated Journal  
  Volume 130 Issue Pages 327-339  
  Keywords Apparel; Computer vision; Structured prediction; Regression  
  Abstract 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.  
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  Area Expedition Conference  
  Notes ADAS: MSIAU; 600.122; 600.118 Approved no  
  Call Number Admin @ si @ SLR2018 Serial 3128  
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