Image segmentation is a computer vision task that segments an image into multiple areas by assigning a label to every pixel of the image. It provides much more information about an image than object detection, which draws a bounding box around the detected object, or image classification, which assigns a label to the object.
Segmentation is useful and can be used in real-world applications such as medical imaging, clothes segmentation, flooding maps, self-driving cars, etc.
There are two types of image segmentation:
Semantic segmentation: classify each pixel with a label. Instance segmentation: classify each pixel and differentiate each object instance. U-Net is a semantic segmentation technique originally proposed for medical imaging segmentation. It’s one of the earlier deep learning segmentation models, and the U-Net architecture is also used in many GAN variants such as the Pix2Pix generator.
Find Explanation and Code here: image_segmentation_code_explanation.pdf