SEMANTIC SEGMENTION:

Semantic segmentation is a computer vision technique that involves dividing an image into multiple segments or regions, each of which corresponds to a particular object or class of objects. The primary goal of semantic segmentation is to enable machines to identify and understand objects within an image, and to assign them specific labels or categories.The process of semantic segmentation typically involves a deep learning algorithm, such as a convolutional neural network (CNN), that is trained on a large dataset of labeled images. During training, the algorithm learns to identify the visual features that are most indicative of particular objects or classes, and to use those features to segment new images.One common approach to semantic segmentation involves using a fully convolutional network (FCN), which takes an image as input and produces a segmentation map as output. The segmentation map consists of a grid of pixels, where each pixel is labeled with the object or class that it belongs to. The FCN architecture typically involves a series of convolutional layers, followed by upsampling layers that gradually increase the resolution of the segmentation map.

• Semantic segmentic
• Image segmentation
• Computer vision
• Deep learning in computer Vision
• Image segmentation using U-net
• Convolutional Neural Networks