Image Segmentation

involves converting an image into a collection of regions of pixels that are represented by a mask or a labeled image. By dividing an image into segments, you can process only the important segments of the image instead of processing the entire image.

• Use cases:

  • Medical image analysis (identifying and segmenting tumors).

  • Robotics and autonomous vehicles (environment perception and scene understanding).

• Algorithms:

  • U-Net, FCN (Fully Convolutional Network), and Mask R-CNN are common architectures for image segmentation.

Semantic Segmentation

is a computer vision task in which the goal is to categorize each pixel in an image into a class or object. The goal is to produce a dense pixel-wise segmentation map of an image, where each pixel is assigned to a specific class or object.

Instance Segmentation

is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object