Overview
Image segmentation provides a more granular understanding of an image than object detection. Instead of bounding boxes, it assigns a label to every single pixel in the image.
Types of Segmentation
- Semantic Segmentation: All pixels belonging to the same class (e.g., all 'trees') are given the same label, without distinguishing between individual instances.
- Instance Segmentation: Distinguishes between different objects of the same class (e.g., labeling each individual 'car' separately).
- Panoptic Segmentation: Combines semantic and instance segmentation to provide a complete pixel-level understanding of the entire scene.
Common Architectures
- U-Net: Widely used in medical imaging.
- Mask R-CNN: An extension of Faster R-CNN for instance segmentation.
- DeepLab: A popular framework for semantic segmentation.
Applications
- Medical image analysis (segmenting organs or tumors).
- Autonomous driving (identifying road boundaries, sidewalks).
- Image editing (background removal).