Image Processing, Image Analysis, and Data Science Applied to Problems in Printing and Semantic Understanding of Images Containing Fashion Items

This thesis aims to address problems in printing and semantic understanding of images.

The first one is developing a halftoning algorithm for multilevel output with unequal resolution printing pixels. We proposed a design method and implemented several versions of halftone screens. They all show good visual results in a real, low-cost electrophotographic printer.

The second problem is related to printing quality and self-diagnosis. Firstly, we incorporated logistic regression for classification of visible and invisible bands defects in the detection pipeline. In addition, we also proposed a new cost-function based algorithm with synthetic missing bands to estimate the repetitive interval of periodic bands for self-diagnosing the failing component. It is much more accurate than the previous method. Second, we addressed this problem with acoustic signals. Due to the scarcity of printer sounds, an acoustic signal augmentation method is needed to help a classifier perform better. The key idea is to mimic the situation that occurs when a component begins to fail.

The third problem deals with recommendation systems. We explored the similarity metrics in the loss function for a neural matrix factorization network.

The last problem is about image understanding of fashion items. We proposed a weakly supervised framework that includes mask-guided teacher network training and attention-based transfer learning to mitigate the domain gap in datasets and acquire a new dataset with rich annotations.