Transparency Detection in Computer Vision Applications

Transparency is all around us. From windows to glass encased shopping malls. From drinking glasses to aquariums. And from safety goggles to protective encasings. It is one of the signs of technological advancement and prosperity, widely utilized in art and engineering. In contrast, it is not clearly understood how humans even perceive transparency. To build advanced autonomous moving agents or vision aids, we need to at least solve this intriguing challenge for computer vision applications. This thesis highlights and examines possible adverse effects that transparency related effects introduce to computer vision applications. Common depth estimation techniques only account for one depth per pixel, while a drinking glass already adds two possible depths. Looking through transparency, distortions and reflections affect object detection methods. Transparent objects transmit light differently, when dust accumulates or scratches interrupt their surface. This leads to gradually worse results from, e.g., condition monitoring setups behind protective glasses. Disregarding human made transparencies, there are also natural phenomena like heat shimmers and water droplets that play a role in industrial applications like condition monitoring or for autonomous agents. Solving those issues is not always possible, due to the ill posed task of restoring a pre transparency influenced image state. However, I introduce several possibilities of detecting and mitigating transparency related effects. A common issue with machine learning related solutions for novel challenges oftentimes lies in the scarcity of suitable data and/or the massive labor cost involved in creating new data from scratch, as in the wider case of transparency. To enable further research concerning transparency related effects on computer vision applications, I introduce a state-of-the-art luminance keying dataset acquisition method that greatly facilitates the translation from real world objects into annotated datasets. It combines a black 99,99\% light absorbing background with a post processing pipeline that leads to automated annotation masks. It is also the first easy setup that works with transparent objects, resolving the most demanding side effects from common chroma keying: no color bleeding, fuzzy edges, foreground background mixing, or shadows. In summary, this thesis showcases the importance and challenging aspects of transparency for computer vision applications and introduces novel prospects to facilitate further research.