1. Segmentation
Segmentation algorithms partition an image into sets of pixels or regions. The purpose of partitioning is to understand better what the image represents. The sets of pixels may represent objects in the image that are of interest for a specific application. The manner in which we partition distinguishes the different segmentation algorithms.
In some applications, we are interested in specific countable objects in a given image. For example, in autonomous navigation, we are interested in instances of vehicles, traffic signs, pedestrians, and other objects on the roads. Collectively, these countable objects are called things. All other pixels are lumped together as background. This type of segmentation is called instance segmentation.
In other applications, we are not interested in countable objects but in amorphous uncountable regions, such as the sky, forests, vegetation, roads, grass, buildings, and bodies of water. These objects are collectively called...
