🎨 Semantic Segmentation
Pixel-level image understanding for advanced computer vision
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Feature Extraction Demo
Understanding Semantic Segmentation
🎯 What is Semantic Segmentation?
Semantic segmentation assigns a class label to every pixel in an image. Unlike object detection which draws bounding boxes, segmentation provides precise, pixel-level understanding of scene composition.
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Key Difference
Classification: "What?" | Detection: "What + Where?" | Segmentation: "What at every pixel?"
🎨 Semantic Segmentation
All pixels of the same class get the same label (all people labeled "person")
- •Class-level labeling
- •No instance distinction
- •Faster inference
👥 Instance Segmentation
Distinguishes between individual objects (person1, person2, person3)
- •Object-level labeling
- •Counts individual instances
- •More complex
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Autonomous Driving
Road, lane, vehicle, and pedestrian segmentation for self-driving cars
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Medical Imaging
Tumor detection, organ segmentation, and disease diagnosis
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Satellite Analysis
Land use classification, crop monitoring, urban planning
📊 Task Comparison
| Task | Output | Granularity | Speed |
|---|---|---|---|
| Classification | Single label | Image-level | Fast |
| Detection | Boxes + labels | Object-level | Medium |
| Semantic Seg. | Label per pixel | Pixel-level | Medium |
| Instance Seg. | Mask per instance | Instance-level | Slow |