AI models analyze pixel patterns, colors, and textures throughout an image. The system groups similar pixels together to form meaningful segments. Input is a digital image. Output is a map showing different regions labeled by category. Three main types exist: semantic (labeling pixel categories), instance (separating individual objects), and panoptic (combining both approaches).
Image Segmentation
Image segmentation divides digital images into distinct regions or segments. Each segment represents different objects, backgrounds, or areas of interest. This process helps computers understand what appears where in an image.
HOW IT WORKS
How Image Segmentation Works
APPLICATIONS
Use Cases
Manufacturing
Quality control systems identify defective parts on assembly lines by segmenting product components from backgrounds.
Healthcare
Medical imaging software segments organs, tumors, and tissues in CT scans and MRIs for diagnosis.
Retail
Smart checkout systems segment individual products in shopping carts to automate billing without manual scanning.
Transportation
Autonomous vehicles segment roads, pedestrians, vehicles, and traffic signs to navigate safely through traffic.
Construction
Drone surveys segment building materials, equipment, and structures to monitor construction progress and safety compliance.
Agriculture
Crop monitoring systems segment healthy plants from diseased areas to optimize pesticide application and yield.
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