What are the limitations of Watershed Segmentation Algorithm?

1. How over segmentation can be avoided in watershed algorithm?
2. What is watershed segmentation method?
3. What is the main objective of watershed algorithm?
4. Which algorithm is best for segmentation?
5. What is marker controlled watershed segmentation?

How over segmentation can be avoided in watershed algorithm?

All Answers (3) The main way to deal with watershed over-segmentation is by designing markers for the objects to be reconstructed.

What is watershed segmentation method?

Watershed segmentation is a region-based technique that utilizes image morphology [16, 107]. It requires selection of at least one marker (“seed” point) interior to each object of the image, including the background as a separate object.

What is the main objective of watershed algorithm?

Watershed algorithms are used in image processing primarily for object segmentation purposes, that is, for separating different objects in an image. This allows for counting the objects or for further analysis of the separated objects.

Which algorithm is best for segmentation?

Clustering-based Segmentation

Popular algorithms like the K-means clustering algorithms are unsupervised algorithms that work by clustering pixels with common attributes together as belonging to a particular segment.

What is marker controlled watershed segmentation?

Introduction. Marker-controlled Watershed is an ImageJ/Fiji plugin to segment grayscale images of any type (8, 16 and 32-bit) in 2D and 3D based on the marker-controlled watershed algorithm (Meyer and Beucher, 1990).