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Applications of AI in Computer Vision

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- Object Recognition

 This branch of computer vision AI involves detecting one or more things in an image or video. For example, surveillance cameras can intelligently identify people and their movements (no movement, items such as guns or knives), so these suspicious activities can be flagged.


- Image Segmentation

 Image segmentation is a pixel-level computer vision technique used to determine what is in a given image. It differs from image recognition, which labels a complete image with one or more labels, and object detection, which locates objects within an image by creating a bounding box around the image. Image segmentation provides finer-grained information about image content.


- Image Classification

Image classification is the process of classifying images based on the visual content surrounding them. This process requires attention to the relationship between adjacent pixels. A database with a predetermined schema constitutes a classification system.

These patterns are compared with recognized objects to determine their classification. Fields such as vehicle navigation, biometrics, video surveillance, and biomedical imaging all benefit from image classification.


- Real-time Enhancement

Augmented reality applications rely heavily on computer vision. The technology enables AR applications to detect physical things (surfaces and individual objects within a physical location) in real time, and use that data to locate virtual objects in the physical environment.


- Face Recognition

The goal of facial recognition technology is to identify objects or faces in photographs. Because of the diversity of the human face—expression, attitude, skin tone, camera quality, position or orientation, image resolution, etc.—it is one of the more difficult applications of computer vision.

However, this method is widely adopted. It is used to authenticate users on smartphones. When Facebook suggests tags for people in photos, it takes the same approach.


- Identify Patterns and Identify Edges

The ability of a system to discover properties or patterns in data is called pattern recognition. A schema can be a cyclic sequence of data or a set of data that has been added to the system.

Finding the edges of objects in a picture is what edge detection is all about. This is accomplished by sensing brightness discontinuities. Edge detection is very useful in data extraction and image segmentation.


- Agriculture

Many agricultural companies use computer vision to monitor harvests and deal with common agricultural problems such as weed growth and nutrient deficiencies. Computer vision systems analyze photos from satellites, drones, and aircraft to detect problems early enough to avoid avoidable financial losses.



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