What is Semantic Segmentation?
Semantic segmentation stands as a cutting-edge technique in computer vision, crucial for unraveling and deciphering the visual content embedded in images. Unlike the traditional approach of assigning a single label to an entire image, semantic segmentation dives deeper by meticulously categorizing each pixel, essentially creating a pixel-level map of distinct classes or objects. The objective is to intricately divide an image into coherent segments, linking each pixel to a specific object or region. This granular approach empowers computers to grasp intricate details and spatial relationships within a scene, fostering a nuanced comprehension of visual information. The versatility of semantic segmentation extends across varied domains like autonomous driving, medical imaging, and augmented reality, where pinpoint accuracy in delineating objects and their boundaries is imperative for precise decision-making and comprehensive analysis. By delivering a meticulous understanding of images, semantic segmentation serves as the cornerstone for an array of advanced computer vision tasks and applications.
PSPNet (Pyramid Scene Parsing Network) for Image Segmentation
Within the intricate landscape of semantic segmentation, the Pyramid Scene Parsing Network or PSPNet has emerged as a formidable architecture by showcasing unparalleled performance in deciphering intricate scenes. In this article, we will discuss about PSPNet and implement it.