When I think about how computer vision differs from human vision, what stands out most is how little meaning computers can extract on their own. To a human, even a blurry image is packed with context, emotion, and symbolism. To a computer, it’s just a grid of pixel values that need to be processed before anything useful can be recognized. This is why the article highlights techniques like frame differencing, background subtraction, and brightness thresholding, which are methods that help a computer separate what is important from what is in the background.
To make these systems work, we often need to modify the image so the computer can interpret it. That might mean fixing the lighting, using high-contrast markers, or limiting the scope of what the computer is supposed to track. In a way, we design the world to fit the algorithm rather than expecting the algorithm to fully match the complexity of the world.
In interactive media, I think this capacity to interpret and track movement opens up exciting new directions for creativity. Computer vision gives artists a way to design works that respond directly to a person’s gestures, which is actually something I used in a project for another IM course. It transforms the audience from passive viewers into active participants, making the artwork something dynamic and alive. By combining human imagination with the computer’s ability to detect patterns, interactive art can become more immersive and responsive than ever before.