Supervised methods
Image segmentation is the process of clustering or partitioning the pixels of an image into a set of regions corresponding to the individual surfaces, object, or parts of the object.
Medical image processing, object detection, face recognition, pedestrian detection, and satellite photo analysis incorporates image segmentation techniques. On top of that, image segmentation techniques can simplify and compress images in size.
There are two ways to approach the image segmentation problem—supervised and unsupervised, that is, by manually defining the number of segments and regions or letting the algorithm entirely do the job.
Seeded region growing
We will start with a supervised approach, an assisted way of solving a problem.
Identifying a simple object
In the following section, we will learn to separate a foreground object, which is a cat, from the rest of the background. For this, consider the following code:
using Images, ImageView, ImageSegmentation
img = load("sample-images...
