Methods for Intentional Encoding of High Capacity Human-Designable Visual Markers
Joshua D. A. Jung, Daniel Vogel · 2018
Previous techniques for human-designable visual markers have focused on small encoding spaces, and assume artists do not need to encode specific bit representations. We present a general framework for human-designable visual markers for artists to encode specific bit representations in large spaces. A three-part study, conducted over three weeks, methodically evaluates the usability of different encoding methods when artists encode specific bit representations. The methods span different shape characteristics suitable for artist encoding (convexity, hollowness, number, size, and distance from centroid) and visualization tools are proposed to aid in this process. We further demonstrate that any of the methods presented may be practically used to encode a URL with the aid of a universally available database like TinyURL (rather than a task-specific database), making human-designable visual markers practical for applications such as advertisements.