Image Segmentation Techniques and Real-World Applications
Image Segmentation Techniques and Real-World Applications. Here you will know that What is meant by Image segmentation. What are main techniques for image segmentation. As well as what are real world application of image segmentation.
What is segmentation?
Image segmentation is the process of partitioning an image into a collection of connected sets of pixels. The goal of segmentation is to simplify change the representation of an image into something that is more meaningful and easier to analyses.
Image segmentation is typically used to locate objects and boundaries like lines, curves in images. It is the process of assigning a label to every pixel in an image.
Three main techniques to do.
- Region based segmentation
- Edge based segmentation
Thresholding is finding histogram of gray level intensity.
- Basic Global Thresholding
- Multiple Threshold
- Variable Thresholding
- Otsu’s Technique
Basic Global Thresholding
- Primarily Segment image use:
- Calculate the average intensity m1 and m2 for the pixels.
- Calculate a fresh threshold:
T = ½(m1 + m2)
- Until the variance between values of T is minor than a predefined parameter.
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Real-world applications of image segmentation.
1. Machine vision
2. Deep Learning
3. Medical imaging, including volume rendered images from CT (Computed Tomography) and MRI (Magnetic Resonance Imaging).
4. Measure tissue volumes
5. Surgery planning
6. Intra-surgery navigation
7. Locate tumors and pathologies
8. Virtual surgery simulation
9. Diagnosis, learning of anatomical structure
10. Pedestrian recognition
11. Object recognition
12. Face detection
13. Face recognition
14. Recognition Tasks
15. Fingerprint recognition
16. Iris recognition
17. Traffic controller schemes
18. Brake light detection
19. Video investigation
20. Find objects in satellite images like roads, forests, crops.
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