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Classifying the objects through computer vision involves classification, object detection, and segmentation.
Image classification helps to recognize the objects and existing properties in an image, while object detection allows one to move one step further and find the accurate position of the object of interest, which is possible through bounding box annotation.
However, with image segmentation, you can recognize and understand what exactly is in the image at pixel level view. In semantic segmentation, each pixel in the image belongs to a single class, as opposed to object detection where the bounding boxes of objects can overlap over each other.
The main purpose of using semantic image segmentation is to build a computer-vision-based application that requires high accuracy. AI-based models like face recognition, autonomous vehicles, retail applications, and medical imaging analysis are the top use cases where image segmentation is used to get an accurate vision.
The objects are shaded with a specific color to differentiate the object from nearby others.
Some of the use cases are:
AI models are used to mine metals and other sea components. Computer-based intelligence is utilized to guide the robots and examine the robot’s sensor signals permitting them to find the metal ores.