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It is a one-dimensional and discrete analog of Fisher’s Discriminant Analysis.Following is the list of practical life applications of Image Segmentation: Machine Vision – Machine Vision is defined as the capability of the machine or computer to see and perceive its surroundings.It is an important application of Image Segmentation.
In case of a single segmentation system works great for all problems that appear to be dim.
Reign based segmentation: Region growing is simply a region-based image segmentation method.
It is also called as a pixel-based image segmentation method as it includes the selection of initial seed points.
Reign based segmentation involves the examination of neighboring pixels of initial seed points and verifies whether the pixel neighbors should be added to the region.
This process is performed in the well-controlled environment so that only desired parts get extracted for further analysis.
For example one can segment a human face from a color video with an algorithm.
When regions of interest do not cover the whole image, we can still perform image segmentation into foreground regions of interest while ignoring background regions.
The two main objectives of segmentation: The first objective is to divide the image into its constituent parts for further analysis.
But in tough cases, such as to extract a complete road network from a grey scale aerial image, the segmentation procedure is not easy to do and might require applications of a great deal of domain a building knowledge.
The next objective of segmentation is to perform a representation’s change.