CNNs changed computer vision because they stopped treating images like flat lists of numbers. An image has structure. Pixels near each other usually matter together. That is exactly what CNNs are built to capture. Core Idea A Convolutional Neural Network is designed for spatial data. Instead of looking at every pixel independently, it scans small regions with filters. Those filters learn useful patterns. Edges. Corners. Textures. Shapes. As layers get deeper, simple visual patterns become higher-level features. The Key Structure A basic CNN flow looks like this: Image → Convolution → Activation → Pooling → Deeper Features → Classifier The important part is convolution. A convolution kernel moves across the image and extracts local features. In simple terms: Kernel + Local Image Region → Feature Value So the CNN does not memorize the whole image at once. It learns reusable visual detectors.…