Our world is awash in visual data, and it’s even how we see and interpret the world ourselves. But far larger than any individual human’s memory – or even the 8 billion-strong cumulative memory of the planet – are the images and videos stored online. Millions every day are uploaded to social media.
They are on our phones and on our computers; cloud and local. This explosion of visual information has created a need to understand it, and this is done with computer vision. This field of artificial intelligence enables computers to “see” interpret and understand the visual world much like humans do.
Computer vision is now beginning to transform industries, and its impact is far-reaching.
Demystifying Computer Vision
Computer vision seeks to replicate (and improve) what we know that the human visual system can achieve. It is complex, like our own, and we don’t always know how the algorithms are working, making it more abstract than our own.
How do we get a machine to understand an image in the first place? The process begins with image acquisition, where cameras/sensors capture visual info. Then comes image processing, where algorithms improve the images to make it easier to understand, like reducing the noise reduction. Feature extraction is next up, where the system ID’s important things within the image, like its edges shapes and colors. Algorithms, often too advanced to even understand (because they have been self-learning), then use these features for pattern recognition.
The Mechanics of Computer Vision
The magic of computer vision lies in its algorithms, but these actually vary by use case and approach. Convolutional Neural Networks are perhaps the most common as they excel at image classification and object detection, which have many industry uses.
Object detection algorithms like You Only Look Once and Region-based CNN identify and locate objects within images, such as drawing bounding boxes around cars or people to detect traffic flow. Image segmentation is an entirely separate technique where algorithms divide images into meaningful regions.
Examples in the Wild
Computer vision is far from just a theoretical concept, though, as it now has many industry uses, meaning it has financial and economic implications. In healthcare, it can use medical images to detect tumors (better than humans in some cases) as well as many other pattern recognition tasks within research. The retail industry can use it for automated checkout, much like the Amazon stores watching you pick things off the shelf. Manufacturing plants use it for quality control, while autonomous vehicles rely heavily on computer vision to drive safely and read the world around them.
Companies Leading the Way
Many companies are driving innovation in computer vision. Tech giants like Google Microsoft and Amazon offer powerful cloud-based services, such as Google’s Cloud Vision API, Amazon’s Rekognition and Microsoft’s Azure Cognitive Services for Vision. These all provide pre-trained models and APIs for developers and are great tools, but perhaps more important is the strategy.
Digitalsense – Computer Vision is a computer vision development company based in Uruguay that specializes in AI. The tech on offer include object detection, face recognition, and OCR capabilities. Their expertise spans multiple industries, from Entertainment and FoodTech to Beauty & Wellness. Successful projects include the likes of Sienz (fruit quality control) and Ulta (makeup try-on experience), where they’ve provided end-to-end solutions from business analysis and R&D to full development and deployment.
Clarifai is another important company. It has a very intuitive platform (not necessarily a consultancy) with a big focus on dev tools. The platform supports around half a million users around the world, processing an unthinkable amount of AI requests each day. Deepomatic is also important within image recognition. They work in a variety of sectors and currently analyze close to 1 million operations every month for major clients like Bouygues Telecom and Swisscom.
The Future of Computer Vision
It’s difficult to predict what will be possible in a year’s time with computer vision, but many consultancies have a best guess. What is certain is that there are no signs of innovation slowing down, and that means digital transformation isn’t only important today, but going forward.
Edge computing will bring much faster processing, and this data will be analyzed closer to the source. On top of that, hopefully, Explainable AI will make models more transparent, meaning we can better tinker with them, but also refine the ethical concerns people have with them. Of course, IoT and robotics only come down in price, meaning it’s computer vision will becoming increasingly accessible to smaller companies and startups.
Computer vision is changing how businesses and their processes are interacting with the environment around them, from understanding more about their foot traffic to avoid workplace hazards. It began as a research field with science fiction connotations, but has quickly had a real world impact. Furthermore, its use goes far beyond saving money (labor) or speeding up processes. Instead, in increasingly more situations, computer vision is outpeforming its human counterparts. Does this story revolve around replacement and unemployment? No, it simply means humans can focus on things they’re good at, while computers take care of the pattern recognition.
