How Does Watershed Segmentation Work?
Watershed segmentation is an image processing technique used to separate different regions of an image based on the topology of the intensity value surface. The fundamental idea is to treat the grayscale image as a topographic surface where light pixels represent mountains and dark pixels correspond to valleys.
The process begins by identifying the local minima in the image, which represent the catchment basins. These basins are the areas that would collect water if the image were flooded. The algorithm then simulates flooding, where water rises from these minima. As the water level increases, different basins will meet and create a watershed line, delineating the regions.
To implement this technique effectively, several steps are involved:
- Preprocessing: Noise reduction techniques such as Gaussian filtering are applied to smooth the image before processing.
- Gradient Calculation: The gradient of the image is computed to enhance the boundaries of the objects.
- Marker Detection: Local minima are marked, serving as the starting points for the flooding process.
- Flooding Algorithm: The algorithm expands from the marked points, forming basins until they merge with neighboring basins.
Watershed segmentation is particularly powerful in separating overlapping objects and distinguishing regions in complex images, making it widely used in various applications within computer vision and artificial intelligence.