impimentation of different segmentation techinques like Region-Based Segmentation, Edge Detection Segmentation, Segmentation based on Clustering, etc in order to understand how image segmentation work.
Algorithm | Description | Advantages | Limitations |
---|---|---|---|
Region-Based Segmentation | Separates the objects into different regions based on some threshold value(s). | a. Simple calculations b. Fast operation speed c. When the object and background have high contrast, this method performs really well |
When there is no significant grayscale difference or an overlap of the grayscale pixel values, it becomes very difficult to get accurate segments. |
Edge Detection Segmentation | Makes use of discontinuous local features of an image to detect edges and hence define a boundary of the object. | It is good for images having better contrast between objects. | Not suitable when there are too many edges in the image and if there is less contrast between objects. |
Segmentation based on Clustering | Divides the pixels of the image into homogeneous clusters. | Works really well on small datasets and generates excellent clusters. | a. Computation time is too large and expensive. b. k-means is a distance-based algorithm. It is not suitable for clustering non-convex clusters. |
Mask R-CNN | Gives three outputs for each object in the image: its class, bounding box coordinates, and object mask | a. Simple, flexible and general approach b. It is also the current state-of-the-art for image segmentation | High training time |