Semantic segmentation involves the categorization of images at a pixel level. It is best described as classifying particular classes of images and differentiating them from other classes using a segmentation mask. This technique enables the meaningful grouping of pixels to identify complex objects. Appropriate groupings can range from roads, people, vehicles, and trees. As such, semantic segmentation determines the relevant features within an image, such as the presence of a traversable road, automobile, or pedestrian. This technology is vital for self-driving cars and robotic navigation systems.
Applications of Semantic Segmentation in Real-Life Situations:
- Semantic segmentation of faces is used to capture details such as eyes, nose, mouth, skin, hair, and background. These segmentations can train computer vision applications to identify a person’s ethnicity, age, and expressions.
- Computers rely on semantic segmentation to detect lane markings and traffic signs.
- Virtual Mirror technology leverages semantic segmentation and lets you virtually ‘try on’ clothes without changing.
- Image segmentation algorithms allow medical personnel to quickly detect abnormalities in clinical scans such as CT or MRI scans – streamlining treatment and enabling doctors to examine more patients within a given time.
