Trace Inequalities
Computer vision related processes like motion detection and facial recognition increasingly extend into daily life at the more personal points of interaction with technology. Digital/smartphone photography is mediated at the point of capture while images are subject to further automated processing if they are used on social media platforms. Similar methods are used in surveillance systems that form part of what are increasingly code reliant public spaces, and market research companies will now pay to install cameras in people’s homes to detect and capture interactions with particular products. In effect, the domestic presence of computer vision can be traced back further if you consider that the optical mouse was the first realised and most widely sold smart camera in existence (Belbachir, 2010).
It is important to understand the points at which we are detectable by devices and algorithms that are integral to the environments we are part of, so that they can be navigated with greater degrees of autonomy. OpenCV (Open Source Computer Vision Library) provides unique opportunities in this respect as it is as much geared towards learning as it is a leading resource used by companies, research groups and governmental bodies. In this instance it has been used to develop introspective cybernetic systems that allow for real time interrogation and the figuring of black boxed processes through aesthetic experience.
Belbachir, A. (2010). Smart cameras. New York: Springer, pp.13-14.
