Moving beyond classifying entire images or drawing bounding boxes around objects, image segmentation aims for a pixel-level understanding of scene content. The goal is to assign a category label to every pixel in an image, providing a detailed mask that outlines the exact shape of objects.

In this chapter, you will learn the fundamental concepts and techniques for image segmentation using deep learning. We'll start by distinguishing between semantic segmentation (labeling pixels by category, like 'road' or 'person') and instance segmentation (labeling distinct object instances, like 'person 1' and 'person 2').

You will study key architectures and methods developed specifically for this dense prediction task, including:

• Fully Convolutional Networks (FCNs) as a foundational approach.
• Encoder-decoder architectures like U-Net, popular in medical imaging and beyond.
• Techniques using dilated (atrous) convolutions, such as those in the DeepLab family, to manage receptive fields.
• Methods that combine detection and segmentation for instance-level results, notably Mask R-CNN.

We will also discuss common evaluation metrics used for segmentation tasks, such as Intersection over Union (IoU), often calculated as $J(A, B) = \frac{|A \cap B|}{|A \cup B|}$, and conclude with practical implementation exercises.