Computer Vision is a simple way to make a task very easy which a normal human vision can do!
But, how does Computer Vision work?
This process of computer vision is dealing with systems that try to take info from images.
The image data can take forms, like video sequences or views from different cameras.
Computer Vision is for processing the images and videos in the surroundings.
Understanding is to the change of the images.
Computer vision deals with the analysis of the image.
In other words, it finds the components in the image and finds the edges in an image.
It is a higher level of image processing where the input is an image and the output is not an image, but the use of the image.
How does Computer Vision work?
Consider Computer Vision AI like the very famous jigsaw puzzle.
There are several pieces that you have to bring together so that you can create a complete image. In the same way, the neural networks for computer vision work. The networks distinguish between different pieces of an image and identify the models.
Instead of giving options to use an object, the computer is filled with images that can help in the detailed identification of an object that you want to create.
For instance, if you have to train a computer to identify a cat.
Instead of giving hints like different tails, whiskers, pointy ears, the system is fed with hundreds of images of cats.
Therefore, the model created learns about different features that make up a cat or differentiate it from other similar looking animals.
3 steps :
Acquiring an image
The first step of computer vision working is that the images can be in real-time through video, photos, or 3D for analysis and further observation.
Processing the image
Deep learning models make easy much of this process, but the models are often trained by first being given thousands of previous images.
Understanding the image
The final step of Computer Vision working is the identification step, where it identifies or classifies the object.
Computer Vision is very important in day to day life!
Because, it can be used for Edge Detection, Pattern Detection, Image Classification, and so on.
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