Explain a typical computer vision pipeline.

A typical computer vision pipeline involves several sequential stages to interpret visual information:

1. Image Acquisition: Capturing raw visual data using cameras or sensors.
2. Preprocessing: Enhancing the image quality and preparing it for analysis. This includes tasks like noise reduction (e.g., using filters), resizing, cropping, color space conversion, and normalization to standardize pixel intensities.
3. Feature Extraction: Identifying and extracting relevant characteristics from the image, such as edges, corners, textures, or more complex patterns using techniques like SIFT, SURF, or deep learning-based feature extractors.
4. Segmentation: Dividing the image into meaningful regions or objects, often by grouping pixels with similar properties.
5. Object Detection/Recognition: Locating specific objects within the image and classifying them.
6. Scene Understanding/Interpretation: Analyzing the relationships between detected objects and the overall context to derive higher-level meaning about the scene.
7. Post-processing: Refining the results, such as applying non-maximum suppression to object detection bounding boxes or generating descriptive text.