A method for sparse disparity densification using voting mask propagation

We describe a novel method for propagating disparity values using directional masks and a voting scheme. The driving force of the propagation direction is image gradient, making the process anisotropic, whilst ambiguities between propagated values are resolved using a voting scheme. This kind of anisotropic densification process achieves significant density enhancement at a very low error cost: in some cases erroneous disparities are voted out, resulting not only in a denser but also a more accurate final disparity map. Due to the simplicity of the method it is suitable for embedded implementation and can also be included as part of a system-on-chip (SOC). Therefore, it can be of great interest to the sector of the machine vision community that deals with embedded and/or real-time applications.