Propelling the momentum of artificial vision in trendsetting technologies like autonomous vehicles, medical imaging and diagnostics, satellite imagery,  robotics, and creativity tools, Image Segmentation plays an important role in Image Recognition systems displaying fine-grain information with its pixel-level understanding.

This technique of computer vision converts a digital image into multiple meaningful image segments called image objects at a granular level with the help of image segmentation algorithms that require large-scale data for training. The labels are assigned to pixels and the labels categorize them as per their common features of color, intensity, or texture. With the help of labels, boundaries are specified and the objects of interest in an image are extracted for further processing. A few examples of Image segmentation applications are the separation of foreground and background of objects, locating tumors, video surveillance, and face recognition. 

However, will this prime domain of computer vision that captures, segments, labels, and processes the visual data require continuous enhancement of data modeling techniques? 

- MADHAVI DESAI