Region-Based Image Segmentation
Region-based segmentation groups pixels or regions based on their similar properties, such as intensity, color, or texture. This approach assumes that pixels within the same region have similar characteristics.
Region Growing
Region growing starts with a seed point and expands the region by adding neighboring pixels that have similar properties. The process continues until no more pixels can be added. Region growing is simple and intuitive, producing connected regions, but it is sensitive to noise and requires careful selection of seed points.
Region Splitting and Merging
Region splitting and merging is a hierarchical method that involves dividing the image into smaller regions and then merging adjacent regions with similar properties. Initially, the entire image is considered as a single region. The region is then recursively split until the resulting regions are homogeneous. Adjacent regions with similar properties are then merged. This technique is effective in handling complex images with varying intensity levels.
Watershed Segmentation
Watershed segmentation treats the image as a topographic surface, where pixel values represent the elevation. It identifies the catchment basins and ridge lines, segmenting the image into distinct regions. The watershed algorithm is particularly useful for separating overlapping objects in an image, making it popular in medical imaging and object detection.
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
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