Whilst we as humans can use our eyes to see and understand what we see, computers and digital technology cannot (obviously).
With cameras, computers gain “eyes”, which when combined with computer vision allow them to comprehend what they see. Computer vision aims to detect objects, segment images into smaller sections, and feature match to detect patterns between images and match the similarities, among other tasks.
Computers can recognise objects in images by using parameters like hue, contrast, saturation, edges, and depth. In doing so, it also recognises the patterns on objects such as logos or text.
Computer vision algorithms are based on pattern recognition. In essence, they compare an image to thousands of others in order to identify commonalities and patterns that indicate that they fit some known category.
For example, if you show a computer an image of a panda, it would not be able to identify it right away. To identify the image the algorithm has to be trained. In order to do that, you would need photos of pandas that have labels describing what each image contains.
To train the algorithm to differentiate pandas from other animals, users can also upload labeled pictures of dogs or cows, and the algorithm can find statistics about which pixels tend to appear together in images of pandas as compared to images of other animals. This process is called supervised learning.
Usually around 100 images are sufficient to train an algorithm. Though this can be a lot less if your images are more distinguishable, like fish compared to pandas.
With computer vision, all types of movements on a playing field can be detected. Tennis serves can now be measured using wearable sensors and high-speed cameras, allowing AI platforms to track their speed, spin, and placement. Football players can be tracked using their jersey colours from a birds eye using object recognition which could help coaches improve the performance of their teams.
Computer vision does not only benefit players themselves, but it is also extremely handy for the videographers of televised sporting events. Using predictive technology, they can adjust the camera angles and select which camera to televise just before the action arrives.
Computer vision technologies are increasingly used by retailers to organise their stores, identify products in photos, identify customers' faces, check receipts at self-checkout terminals, and more. By detecting what products are picked up and returned to shelves, or by helping consumers locate items, the technology is intended to make shopping faster and easier for shoppers. On the other side of the spectrum, retailers also aim to improve efficiencies by monitoring their shoppers' interactions with products, as well as reducing personnel costs.
Data about customer engagement with products on shelves can be collected during real-time checkouts by retailers who use computer vision. With special cameras, marketers can recognise when shoppers touch or pick up items, providing valuable insights about popular items, when people are most likely to buy products, and even how to draw attention to displays.
The use of computer vision has already become widespread in agriculture, with applications ranging from growing crops to raising livestock.
Automated milking robots operate with cameras that allow them to view where they are in relation to their environment and identify each cow's udder. The identification enables individual cows to be milked during their designated time slot and prevents cross-contamination.
Additionally, drones and satellites are being used to detect droughts, disease, and infestations in crops. Combining these images with machine learning algorithms can allow farmers to make data-driven decisions about when to harvest their crops, often saving hours, if not days, of time.
The main purpose of computer vision in transportation is to detect objects. It is used for all kinds of applications, from recognising that you are tired behind the wheel, to full self-driving capabilities in autonomous vehicles.
Bio-tech startups are utilising computer vision for entirely new applications where image monitoring is used to detect changes in tumours, arteries, organs, and blood flow. This latter approach is known as precision medicine and has been made possible thanks to advances in image analysis and machine learning over recent years.
The way a company handles service and maintenance can have a profound impact on the perception of their brand in the eyes of their customers. Through the use of visual search technology, a type of computer vision, people can easily identify a specific item like a machine part just by taking a photo with their mobile device.
With more than a billion smartphones in use today, customer expectations around a service are rising. When consumers purchase a new piece of machinery, they want to know that they will receive great support for years to come. Making sure they feel confident in that assurance will ultimately lead to repeat business and customer loyalty.
Computer vision technology has come a long way in just a few short years. It represents a way to improve efficiency in an organisation and by using a visual search, workers will be able to perform their duties more efficiently. Starting out as a complex system that required extensive research, today's computer vision applications are more streamlined, smarter, and easier to use than ever before.