Instance segmentation is an advanced image recognition technique in computer vision that seeks to parse an image into pixels, categorize those pixels into discrete objects, and identify the boundaries between them. It combines object-detection methods with semantic segmentation and involves using Convolutional Neural Network (CNN) algorithms to detect objects within an image, followed by a series of additional models to accurately distinguish individual objects and delineate the edges between them. This procedure enables each object to be individually labeled so that specific tasks such as detection, localization, tracking, and classifying can be performed on each unique object separately. Consequently, instance segmentation has become increasingly essential in more sophisticated applications like autonomous navigation or robotic control.
Instance Segmentation enables us to map each pixel in an image into specific classification. Segmentation are done by partitioning an image into different parts or regions. While Semantic segmentation creates a meaning with labels and the just segmentation give no information but just a partition of images. Semantic segmentation puts the images into a predefined semantic classification pixel-by-pixel. This tedious work is simple and ease the process by using Infizoom annotation tools.