- What are some of the ways that computer vision differs from human vision?
Human vision involves cognitive processes that allow us to interpret context, recognize objects without any limitation to the lighting conditions and angles, and also to make intuitive inferences. In contrast, computer vision relies on complex algorithms that analyze pixel data without context or intuition. Unlike human vision, which naturally adapts to varying conditions, computer vision relies on structured methods such as frame differencing, background subtraction, and brightness thresholding to detect motion, presence, or objects of interest.
- What are some techniques we can use to help the computer see / track what we’re interested in?
As recorded in the paper, one of the greatest challenges in computer vision is enabling computers to make accurate detections and distinguish between “what is” and “what was”— key factor in motion and presence detection. Several techniques help achieve this: Frame Differencing: This method detects motion by comparing differences between consecutive frames, identifying areas where pixel values have changed. Background Subtraction: This technique captures an image of an empty scene as a reference and then compares incoming frames against it. Any changes are flagged as new objects. However, it is highly sensitive to lighting variations. Brightness Thresholding: Controlled illumination and surface treatments (such as using high-contrast materials or backlighting) help distinguish objects based on their brightness levels, making tracking more effective in interactive environments. By combining these methods, computer vision can better track motion, recognize objects, and adapt to artistic applications
- How do you think computer vision’s capacity for tracking and surveillance affects its use in interactive art?
The surveillance capacity and tracking ability of computer vision can be used to store and present anomalous data in a creatively artistic way. Many artists have integrated these capabilities to create interactive installations that respond to human movement and behavior. Myron Krueger’s Videoplace (1969-1975), for example, allowed participants to interact with digital graphics using only their silhouettes, demonstrating how computer vision can enable body-driven interaction. Similarly, Messa di Voce (2003) used head-tracking and speech analysis to create a dynamic visual experience where graphics appeared to emerge from performers’ mouths, merging performance with real-time digital augmentation.