Vision
Semantic segmentation
Semantic segmentation is a computer vision task that involves assigning a semantic label to each pixel in an image. Unlike image classification, which predicts a single label for the entire image, or object detection, which identifies bounding boxes around objects, semantic segmentation provides a pixel-level understanding of the scene.
Explanation
Semantic segmentation works by classifying each pixel of an image into a predefined set of categories (e.g., person, car, road, sky). This dense prediction task requires models to not only identify objects but also delineate their precise boundaries. Common architectures used for semantic segmentation include fully convolutional networks (FCNs), U-Net, and DeepLab. These models typically employ an encoder-decoder structure. The encoder downsamples the image to capture high-level features, while the decoder upsamples the feature maps back to the original image resolution, assigning a label to each pixel. Semantic segmentation is crucial in various applications, including autonomous driving (understanding the road scene), medical image analysis (identifying tumors or organs), and satellite imagery analysis (land cover classification). It is a fundamental step towards enabling machines to 'see' and interpret images in a human-like manner.