Image Segmentation

Image Segmentation is the computer vision task that classifies every single pixel in an image, precisely delineating object boundaries for machine analysis.

This technology is critical for high-precision visual data interpretation (pixel-level classification). It operates primarily in two modes: Semantic Segmentation (labeling all pixels of a class, e.g., all 'road') and Instance Segmentation (distinguishing individual objects of the same class, e.g., 'Car 1' vs. 'Car 2'). Key applications drive real-world systems: autonomous vehicles rely on it for real-time pedestrian and lane detection; medical imaging uses it to isolate structures like tumors or organs from CT/MRI scans. Deep learning models, including U-Net and Mask R-CNN, execute this process, providing the spatial accuracy necessary for advanced AI functions.

https://superannotate.com/blog/image-segmentation-detailed-overview