In computer vision, the task of panoptic segmentation involves three distinct steps: separation of each object in an image into individual parts that are independent of one another, painting of separate parts with different colors for labeling, and classification of the objects. The purpose of panoptic segmentation is to unify the two tasks of object detection and semantic segmentation into one overall mechanism.
Objects extracted from an image are classified into two categories: things and stuff. Things refer to objects with well-defined geometry and are countable, such as people and cars. On the other hand, stuff is characterized more by texture and material, for example, the sky or water bodies.
How Is Panoptic Segmentation Used?
- Radiologists can use panoptic segmentation to easily recognize tumor cells in their workflows, as the algorithm enables detection of the foreground and background.
- Autonomous vehicles can benefit from this method’s distance-to-object estimations for better steering, braking, and acceleration decisions.
- Panoptic segmentation can also power features like Portrait mode, Auto-focus, and Photomanipulation in smartphones.
- AR applications leverage panoptic segmentation to add virtual objects to the scene in real time.
- Finally, it can be employed to analyze videos in real-time, such as detecting objects in security camera feeds or tracking player movements in sports footage.
