What is Computer Vision?
An introduction to Computer Vision
In this article, I’ll give you an overview of computer vision, explain how it functions, and go through some of its applications.
So let’s get started.
What is Computer Vision?
The study and application of digital systems for the analysis, processing, and interpretation of visual input is known as computer vision (CV), a subfield of artificial intelligence (AI). Computer vision aims to make it possible for computer equipment to accurately recognise an object or person in a digital image and execute the proper action.
Deep Learning
The best approach for computer vision is deep learning. It employs a neural network method. Neural networks are used to extract patterns from the data. Algorithms are based on our present understanding of the anatomy and function of the brain, particularly the connections that exist between neurons.
What is a Convolutional Neural Network (CNN)?
Convolutional neural networks (CNNs, or ConvNets) are a type of artificial neural network used to analyse visual imagery. A shared-weight architecture of convolution kernels or filters that slide along input features and produce translation equivariant responses is used to construct feature maps.
Convolutional neural networks outperform other neural networks when given inputs such as images, voice, or audio, for example. Convolutional Layer, Pooling Layer, and Fully-Connected Layer are their three major varieties of layer.
More on Convolutional Neural Network (CNN) in the article below.
How does computer vision work
We provide a dataset of photos for teaching computer vision. For instance, in the example that follows, a model that was trained on images of cats is used. The model’s objective is to determine whether or not a cat is present in the supplied image. This general explanation of these models’ (CNN’s) working.
In depth
In the above example we have a circle and we have to predict the shape. So, in order to predict the shape, we must feed the model an example of what a circle looks like. The model’s training converts the image into pixel brightness, which runs from 0 to 255 (0 indicates white and 255 means black), and using the given range, it finds patterns in the photos.
After training on the dataset, the model is familiar with the pattern that represents how a circle will appear. By applying that pattern, the model would be able to predict the shape of the image.
Applications of Computer Vision
- Image recognition
- Self driving cars
- Object detection
- Augmented reality
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Conclusion
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