Reading Computer Vision for Artists and Designers made me think about the stark differences between how humans and computers perceive the world. Human vision is incredibly contextual—we naturally filter out distractions, infer meaning, and recognize objects even when they are partially obscured. In contrast, computer vision relies on algorithms and mathematical models to process images. It doesn’t “see” in the way we do; instead, it detects patterns, edges, and contrasts based on pixel values. I found it fascinating how much work goes into making computers interpret images in ways that feel natural to us.
One of the key techniques for improving computer vision’s ability to track what we’re interested in is feature detection—using algorithms like edge detection, motion tracking, and machine learning models trained on labeled datasets. I also noticed how lighting, contrast, and background control play huge roles in making computer vision more accurate. This made me reflect on its implications for interactive art. I think computer vision’s capacity for tracking and surveillance creates an interesting tension in artistic practice—it allows for dynamic, responsive installations, but it also brings ethical concerns. I felt a bit unsettled thinking about how the same technology that enables playful, immersive experiences can also be used for surveillance. It makes me wonder how artists can challenge or subvert these systems in creative ways.