Five Common Image Segmentation Techniques
Image segmentation is a crucial technique in computer vision, allowing for the division of an image into meaningful segments for easier analysis and interpretation. There are various methods to achieve image segmentation, each with its strengths and applications.
Here, we explore five common image segmentation techniques: threshold-based segmentation, edge-based segmentation, region-based segmentation, clustering-based segmentation, and artificial neural network-based segmentation.
Image Segmentation Approaches and Techniques in Computer Vision
Image segmentation partitions an image into multiple segments that simplify the image’s representation, making it more meaningful and easier to work with. This technique is essential for various applications, from medical imaging and autonomous driving to object detection and image editing. Effective segmentation enables precise identification and localization of objects within an image, facilitating tasks like feature extraction, pattern recognition, and scene understanding.
The article aims to explore the approaches and techniques used for image segmentation in the computer vision domain.
Table of Content
- Image Segmentation Approaches
- Similarity Approach
- Discontinuity Approach
- Five Common Image Segmentation Techniques
- 1. Threshold-Based Segmentation
- Global Thresholding
- Adaptive Thresholding
- Otsu’s Method
- 2. Edge-Based Image Segmentation
- Sobel Operator
- Canny Edge Detector
- Laplacian of Gaussian (LoG)
- 3. Region-Based Image Segmentation
- Region Growing
- Region Splitting and Merging
- Watershed Segmentation
- 4. Clustering-Based Image Segmentation
- K-means Clustering
- Mean Shift Clustering
- Fuzzy C-means Clustering
- 5. Artificial Neural Network-Based Segmentation
- Conclusion