It's really no surprise that artificial intelligence (AI) is around us, but it's not always apparent how we should engage with it, let alone what precise approaches are at work.
One subgroup is easy to identify: if the experience is intelligent and includes images or videos, or is visually oriented in any manner, computer vision is almost probably at work behind the scenes.
Computer vision (CV) is the subcategory of artificial intelligence (AI) that focuses on building and using digital systems to process, analyze and interpret visual data.
Computer vision uses convolutional neural networks (CNNs) to process visual data at the pixel level and deep learning recurrent neural networks (RNNs) to understand how one pixel relates to another.
Computer vision enables computers to do a wide range of jobs. Picture segmentation (which separates an image into pieces and investigates each one separately) and pattern recognition are two examples (recognizes the repetition of visual stimuli between images).
Object classification (classifies items detected in an image), object tracking (finds and tracks moving things in a video), and object detection are all available (looks for and identifies specific objects in an image).
There's also facial recognition, a sophisticated sort of object detection that can detect and recognise human faces.
As previously stated, computer vision is a branch of machine learning that uses neural networks to filter through huge volumes of data until it knows what it's looking at.
For example, consider how deep learning may be used to distinguish between photographs of burgers and pizzas, which is a computer vision use case. You provide the AI system a plethora of images portraying both food items.
The computer runs the images through numerous layers of processing — which comprise the neural network — one by one to identify the element from the other.
Earlier layers examine simple picture attributes such as lines or boundaries between bright and dark areas, whereas succeeding layers recognise more sophisticated elements such as forms or even faces.
This works because computer vision systems perceive an image (or video) as a series of pixels, each of which is labeled with a color value. These tags are used as inputs by the system as it advances the picture through the neural network.
Summing up
Computer vision is an innovative field that uses the latest machine learning technologies to build software systems that assist humans across different fields.
From retail to wildlife conservation, smart algorithms solve the problems of image classification and pattern recognition, sometimes even better than humans.
