Computer vision works by processing visual data through a series of steps: capturing images, preprocessing them (e.g., resizing or filtering), and extracting features like edges or textures using algorithms or neural networks.
Deep learning models, particularly convolutional neural networks (CNNs), learn patterns from training data to recognize objects, classify images, or perform other tasks. These models interpret visual input hierarchically, from simple patterns to complex objects or scenes.
The output can include labels, bounding boxes, or pixel-wise segmentation, enabling diverse applications like autonomous navigation, medical diagnostics, and real-time video analytics.