Computer vision consulting services fall under the umbrella of artificial intelligence (AI). CV is a rapidly evolving discipline that’s used across many industries, from healthcare and agriculture to manufacturing and transport.
CV is all about seeing, observing, and understanding. It allows computers and systems to extract meaningful and real-time information from videos and images. That info is then used alongside other machine learning techniques to take actions or make recommendations.
The technology focuses on replicating human perception, so computers not only accurately identify and classify objects, but react to them as well.
Where humans automatically combine sight with context, CV trains machines to carry out these activities using cameras and specialized algorithms.
Trained systems are able to analyze thousands of images a minute, and pick up defects and problems. Such systematic accuracy means humans no longer need to carry out certain tasks.
Firstly, computer vision projects need lots of data – images and videos. Next, it analyzes that data over and over picking out specific characteristics, enabling image recognition. To achieve that, enter two technologies: Deep learning and convolutional neural networks (CNNs).
How does Image Analysis work?
Via deep learning algorithms, computers ‘teach themselves’ to contextualize images and videos. By feeding lots of data through the models, they’re able to distinguish one image from another.
A developer could program a computer to recognize an image, but by using an algorithm, the computer or machine learns by itself. A convolutional neural network is a type of deep learning model.
These models work by understanding and analyzing visual imagery, and breaking it down into features such as lines, corners or more complex objects like cars. The features are used during training by the neural network for learning, to “see” (creating proper features) and distinguish objects or images.
We use convolution layers, because they’re best suited for image data, allowing fast and reliable processing. The models aren’t a black box, rather something we use to interpret results. We analyze and intepret based on what part of the image we used to make a certain decision.
How computer vision can help retailers?
Computer vision solutions and retail go hand in hand, from improving customer experience to inventory tracking.
Another way retailers can use CV is to streamline operations by installing a CCTV camera (or using existing infrastructure) combined with intelligent video analytics.
The AI-driven solution processes footage and serves as a real-time tool to optimize shelf-space, plan inventory more efficiently, or cut footprint. By doing that, retailers gain a competitive advantage.
Smart video analytics solutions can also track and analyze customer behavior, helping improve sales strategies and boost customer retention.
We use deep learning models, CNNs, and vision transformers to help retail clients achieve their goals, and are on hand with cost-minimizing advice.
How does GDPR applies to computer vision?
The introduction of GDPR (General Data Protection Regulation) impacted how organizations can handle personal data like images or photos.
Companies must adhere to the regulation and be GDPR-compliant. From a legal perspective, a lot depends on what data is extracted and used, and where it’s sent.
For example, using CV for people counting is allowed, but building individual customer profiles based on visual appearance is illegal.
To comply with privacy directives like GDPR, CCPA, APPI, and CSL, anonymization software comes into play, protecting personal information in images.
For example, redaction software can help healthcare companies using CV keep in line with specifications like HIPAA. Most EU countries have individual strategies for regulating AI that are broadly similar.
More structured regulation is to come, evidenced by the Artificial Intelligence Act outlined by the European Commission. The Commission proposes a risk-based approach, with four levels of risk.
Rest assured, Netguru follows all legal requirements regarding the processing information, and will continue to do so as the regulatory landscape evolves.
How is computer vision used in AR?
For augmented reality to work, we need to make sense of what a camera can see, and estimate the depth to properly overlay computer-generated images onto the real world.
Via CV tasks such as object detection and object tracking, it’s possible to identify what and where it is.
For example, Instagram uses computer vision to recognize people tagged in images, CV enables biometric bank account login using your eyes, computer vision alongside AR enables users to add filters to their face in Snapchat or Messenger.
Moreover, AI coupled with computer vision and AR helps surgeons make more precise incisions; CV and AR help elevate e-commerce shopping experiences by overlaying products in a buyer’s home; and both technologies are part of the virtual try-on phenomenon, too.
