Deep learning has been very successful when working with images as data and is currently at a stage where it works better than humans on multiple use-cases. The most important problems that humans have been interested in solving with computer vision are image classification, object detection and segmentation in the increasing order of their difficulty.
In the plain old task of image classification we are just interested in getting the labels of all the objects that are present in an image. In object detection we come further a step and try to know along with what all objects that are present in an image, the location at which the objects are present with the help of bounding boxes. Image segmentation takes it to a new level by trying to find out accurately the exact boundary of the objects in the image.
In this article we will go through this concept of image segmentation, discuss the relevant use-cases, different neural network architectures involved in achieving the results, metrics and datasets to explore.
Don't forget to tag @Anil-matcha in your comment, otherwise they may not be notified.