vision

Image acquisition: The first step is to acquire the visual data, which can be done using cameras, drones, satellites, or other sensors.
Preprocessing: The raw image data is then preprocessed to improve its quality and prepare it for further analysis. This may involve tasks such as noise reduction, cropping, and resizing.
Feature extraction: The preprocessed image is then analyzed to extract relevant features, such as edges, colors, textures, and shapes. These features are essentially the building blocks that the AI system will use to understand the content of the image.
Classification or recognition: The extracted features are then used to classify the image or recognize objects within it. For example, an AI vision system could be used to classify an image as containing a cat, a dog, or a car.
Action or insight: Based on the classification or recognition results, the AI system can then take an action or generate an insight. For example, an autonomous car equipped with AI vision could use the information to identify and avoid obstacles on the road.