Artcode detection in images - Xu, Liming (2020)
We are in a world of thinly disguised aesthetic visual markers. How do people know of their presence is the initial step into this augmented world. This thesis is concerned with such special aesthetic markers — Artcodes, which are topological markers that implement and extend the d-touch system. While current Artcode applications focus on the activities after decoding, this thesis examines the activities before decoding, investigating the question of how to discover such “invisible” visual markers as Artcodes. Interacting with an invisible sensing system (vision-based AR system here) is a longstanding question in the HCI community. This question is proposed as a formal computer vision problem — Artcode detection, including two dependent subproblems: Artcode classification (classifying an input image or image patch as either containing an Artcode or not) and Artcode localisation (predicting the locations of Artcodes in an image), and addressed using machine learning methods.