13 Nov · 4 min read
Images and videos were a no longer just vacation reminders. Even though they were often associated with amusement, but they can also be used to contain data that can be analysed and evaluated. Here's how to use computer vision, one of today's trendiest AI technologies, to understand visual media.
Computer vision is a subset of AI that focuses on visual analysis and interpretation. We can use computer vision to search photos and movies for specific information, patterns, and categorisation.
The easiest approach to grasp computer vision is to conceive of it as a human-like vision system. The same goes for a car: we've seen it innumerable times before, know its appearance, and can thus identify it without difficulty.
Using deep algorithms is the fastest way. To teach a machine to accurately categorise photos, we need to provide it examples of correctly tagged images. So the system can recognise patterns between images and properly identify what it's "looking at". It can quickly learn from mistakes and improve its accuracy with enough annotated training data.
Deep learning algorithms are intriguing, but they don't explain why we need computer vision. We can use it in so many ways!
We know that computer vision can automate security and monitoring systems. It can be trained to check for suspicious activity because it can distinguish items in visuals. Because a machine can view hours of footage without getting weary, it can be considerably more accurate than human eyes and warn us when something is wrong.
This is where computer vision shines. It can not only organise visual data by adding relevant tags, but also help us find what we're seeking for. It's very useful for content moderation. We can use computer vision to search for inappropriate information in photographs or videos, automating a lot of human labour.
Computer vision is very useful for automating facial recognition and fingerprint identification. Most of us have seen this on our cellphones or other digital gadgets, but it goes much beyond that. Many businesses utilise computer vision to verify client identity. It's arguably one of the most important applications of computer vision, and also one of the most disputed.
We briefly highlighted security anomalies, but it extends well beyond that. Because computer vision algorithms are supplied data on how something should seem, they may detect any visual fault, such as a scratch, wrong colour, size, or shape. However, this is one of the most promising applications of computer vision for manufacturing companies.
Have you ever had to manually convert text to digital? Similar time-consuming jobs can be eliminated with computer vision. Then we can digitise and turn them into a machine-readable format using optical character recognition (OCR). This improves data organisation and modernises internal operations.
Planter is a real-world example of computer vision. The mobile app uses object recognition to identify plant species and provide maintenance instructions.
The most vital industry for us all also offers the most potential for growth and benefit from computer vision. It can assist speed up and enhance medical imaging diagnostics by detecting characteristics in images. A computer vision model can be trained to analyse ultrasound, x-rays, MRIs, etc. Computer vision has huge potential in healthcare, as it can already diagnose breast cancer more accurately than the human eye.
We touched on this one when discussing defect detection, so let's expand on it. Aside from detecting defective products, computer vision may also identify product parts and assist the machine in assembling them, as well as monitor the surroundings for potential compliance violations.
The financial industry is becoming increasingly digital, and this tendency will continue. Security standards must, of course, closely follow innovation. Identity verification becomes significantly faster and the system can function 24/7 with computer vision. Mobile payments utilising facial recognition may become a standard in the future.
Product photos and videos are vital to the e-Commerce market, and computer vision can help online store operators. It's especially useful for product suggestions, as a computer vision system can automatically categorise photographs and use that data to create a better tailored offer. That way, no blurry product photos are left on the page.
For any entertainment website or app, computer vision solutions are especially useful for social media, where the content keeps piling up daily. Computer vision can automatically recognise visual content that violates community standards, making social media safer. This allows the system to erase it before any user sees it.
With enough data, computer vision can be used almost any place where there is visual material. It can help you automate operations like anomaly detection, monitoring, and identity verification. We are still investigating the full capabilities of this technology, but we can already claim that it is here to make our life a little easier.