Classifying the objects through computer vision involves, classification, object detection and segmentation.
Image classification helps to recognize the objects and existing properties in an image, while object detection allows to move one-step further and find the accurate position of the object of interest, which is possible through bounding box annotation.
However, with image segmentation, you can recognize and understand what exactly is in the image at pixel level view. In semantic segmentation annotated images, each pixel in image belongs to a single class, as opposed to object detection where the bounding boxes of objects can overlap over each other.
The main purpose of using semantic image segmentation is build a computer-vision based application that requires high accuracy. AI-based models like face recognition,autonomous vehicles, retail applications and medical imaging analysis are the top use cases where image segmentation is used to get the accurate vision.
To annotate images in semantic segmentation, outline the object carefully using the pen tool. Make sure touch the another end to cover the object entirely that will be shaded with a specific color to differentiate the object from nearby others.
Annotators can use the drawing pen tool, to outline the shapes in a speedy manner. This tool allows to draw freehand as well as straight lines, just like polygon tool and there is also option to erase the inaccurate outlines.
Our team is expertise in both Semantic and Instance Segmentation.