In Computer Vision for Artists and Designers by Golan Levin, the main difference between human and computer vision is that while humans naturally interpret context and meaning, computers analyze images numerically.
People can recognize objects in varying conditions, but computers need specific training to do the same. For example, while we can recognize a face even if it’s partially obscured, computer vision would struggle unless it’s specifically trained to do so. This difference stood out to me because it shows how much more complex human perception is compared to a computer’s mechanical analysis.
To help computers track things we’re interested in, techniques like thresholding (simplifying images), edge detection (finding shapes), and optical flow (tracking movement) are used. More advanced methods, like machine learning, help computers recognize objects by learning from large datasets. These methods don’t give the computer understanding, just the ability to process data.
Levin also explores how CV’s tracking abilities are used in both art and surveillance. Artists use CV for interactive installations that respond to movement, but the same tech is used for facial recognition and monitoring. This can be a bit problematic as while CV enhances art, it also enables tracking people, sometimes without consent. Some artists challenge this by confusing or exposing surveillance systems.
This made me think about how CV has become so embedded in our everyday lives, from facial recognition on our phones to tracking in stores. While these systems often make things more convenient, they also normalize constant monitoring. For artists, I think it’s important to be aware of the implications of using CV, especially for privacy and surveillance.