Image segmentation is a computer vision technique where an image is divided into multiple segments or regions to simplify analysis.
Unlike object detection, which draws bounding boxes, segmentation labels each pixel to show exactly which part belongs to which object.
How Image Segmentation Works
- Image Input: Provide an image to the AI model.
- Feature Extraction: Use CNNs or deep learning models to identify patterns and edges.
- Pixel Classification: Assign each pixel a class label (e.g., car, road, person).
- Postprocessing: Refine edges and reduce noise for clear segmentation maps.
Advantages of Image Segmentation
- Provides precise object boundaries
- Enables detailed scene understanding
- Improves performance in medical imaging and autonomous vehicles
- Supports augmented reality and image editing
Disadvantages
- Requires large labeled datasets
- Computationally intensive
- Sensitive to lighting and image quality
Real-World Examples
- Autonomous vehicles: Road and lane segmentation
- Medical imaging: Detecting tumors and organs
- Satellite imagery: Land cover and urban area mapping
- Photo editing: Background removal and object isolation
- Robotics: Scene understanding and navigation
Conclusion
Image segmentation gives AI the ability to understand images at a pixel level, enabling precise analysis and improved decision-making in vision-based applications.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/