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Image Segmentation Types and Applications
geeksforgeeks.org/computer-vision/semantic-segmentation-vs-instance-segmentation/Yapay zekadan makale özeti
- Image Segmentation Overview
- Image segmentation divides images into regions based on color, intensity, texture or proximity
- Main types include semantic, instance and panoptic segmentation
- Semantic Segmentation
- Classifies each pixel into specific categories like objects or background regions
- Uses U-Net, FCN, DeepLab, PSPNet and SegNet techniques
- Aids in scene understanding, autonomous driving and medical imaging
- Instance Segmentation
- Combines object detection and semantic segmentation for detailed pixel-level analysis
- Uses Mask R-CNN, Faster R-CNN, SOLO and YOLACT techniques
- Enables accurate object detection and scene understanding
- Essential for robotics and autonomous systems
- Key Differences
- Semantic segmentation provides holistic understanding, instance segmentation focuses on individual instances
- Semantic segmentation classifies broad categories, instance segmentation assigns unique labels
- Semantic segmentation uses convolutional layers, instance segmentation uses bounding boxes