Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. By using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects - and then react to what they see.
How Computer Vision work
In practice, computer vision combines machine learning (especially deep learning) with other disciplines like computer graphics, pattern recognition, and computational biology to interpret image content.
The process generally goes as follows: First, the system receives input of an image or video. The image is then processed to identify significant components. The type of processing can significantly vary and depends largely on the requirements of the specific project or system.
In the next step, the system might use machine learning algorithms to classify the processed image or video. For instance, the system could identify whether the image contains a specific object or classify it as a specific type of scene.
The final component of computer vision involves making some decision based on the classification. For example, if the system identifies a pedestrian in a self-driving car's video feed, the associated decision would be to apply the brakes.
It's worth noting that computer vision isn't a simple, linear process. Often, systems use feedback from the final decision component to adjust how the initial processing is done or how the image is classified.
Computer vision is a very active research field, and its techniques are applied in a growing number of practical applications, from autonomous vehicles to medical image analysis.
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