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Computer vision is a type of artificial intelligence that allows computers to interpret and analyze the visual world, simulating the way humans see and understand their environment. It applies Machine Learning (ML) models to identify and classify objects in digital images and videos, then allows computers to react to what they see. The different types of computer vision models include image segmentation, object detection, face recognition, edge detection, pattern detection, image classification, and feature matching. The 2 main you need to understand will be described further below.

1. Image Segmentation. The first types of computer vision subjects that we will explain here are image segmentation or image segmentation. In short, image segmentation is the process of dividing an image into different regions based on pixel characteristics to identify objects or boundaries to simplify images and analyze them more efficiently. The segmentation referred to here has an impact on several domain areas, from the filmmaking industry to the medical field. For example, the software behind the green screen implements image segmentation to crop the foreground and place it in the background for a scene that cannot or would be dangerous to shoot in real life.

b. Object Detection (Recognition). The next type and type of computer vision is object detection (recognition) or object recognition and detection. Object detection or recognition is one of the sub-sections and techniques of computer vision that allows us to identify and locate objects in images or videos. With this type of identification and localization, object detection can be used to count objects in the scene and determine and track their precise location, while accurately labeling them.

Just imagine, for example, an image containing 2 (two) monkeys and 1 (one) person, with object detection, then it is also possible for us to simultaneously classify the types of things found while also finding their examples in the image.