Understand the visual world with computer vision


Boost business process efficiency and automate tasks by extracting real-time data from videos and images
computer_vision_services

Leverage AI techniques to see, observe, and understand objects and images

Computer vision (CV) uses deep learning and convolutional neural networks to improve products and reduce costs. Use cases are far-reaching, from smart cars, surveillance, and inventory management to crime detection, biometrics, and disease diagnosis.

Improve your offering with customized AI products


Simplify and speed up business processes, minimize errors, and amplify efficiency with computer vision.
  • Reduce redundant manual labor

    Automate repetitive and monotonous tasks and streamline processes.

  • Increase operational accuracy

    Leverage AI solutions to improve efficiency and deliver enhanced products.

  • Tailored business solutions

    Build bespoke and tech-forward products to suit individual requirements.

  • Lower costs

    Automate tasks and reduce errors, decreasing money and time spent fixing flawed processes.

  • Security compliance

    Adhere to GDPR, RODO, and other regulatory requirements, ensuring privacy and transparency.

  • Unique customer experience

    Embrace computer vision to enrich the consumer journey and retain clients.

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Artificial intelligence is the new electricity
andrew_ng_computer_vision

Andrew Ng

Co-Founder of Coursera & Stanford CS Adjunct Faculty

Understanding objects in digital images and videos


Extracting high-dimensional real-world data to improve efficiency and automate.
  • Image recognition

    Analyzing and classifying an image, and predicting the class of objects within

  • Semantic segmentation

    Associating every pixel with a class, with multiple objects of the same class as single entity

  • Object detection

    Using classification and localization to detect the position of objects and their classes

  • Instance segmentation

    Highlighting multiple objects of the same class as distinct and individual

  • Video analysis

    Leveraging intelligent analytics for real-time object detection and task automation

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Derive image insights with computer vision

Computer vision is a technology that allows software to recognize and classify images just like humans do.

Our experienced Machine Learning team has a strong background in computer vision, allowing Netguru to apply advanced Deep Learning techniques in our image recognition projects.

We can deploy them to any platform: mobile devices (Android and iOS), IoT (Internet of Things), the cloud, and more.

How we work?

  1. Gather data that we then use to train our Deep Learning model

  2. Consider all relevant factors, depending on the target platform: Mobile, cloud, IoT, or a combination

  3. Clarify requirements, ensuring the project delivers business value to clients

  4. Accurately train the custom-made deep learning model to automatically recognize images


What is computer vision and how does it work?

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.

Cutting-edge AI solutions that empower your business

Our experienced software engineers possess deep expertise to develop custom solutions. What’s more, our talented developers are open source contributors, with numerous repositories on GitHub.

See how our support helped those companies

  • My experience working with Netguru has been excellent. Outstanding software teams are resilient, and our developers at Netguru have certainly proven to be that. Our Netguru friends have become as close to team members as possible, and I am grateful for the care and excellence they have provided.
    Gerardo_Bonilla

    Gerardo Bonilla

    Product Manager at Moonfare
  • Whenever we faced challenges this year, we could rely on Netguru for our urgent staffing needs and time-critical deliverables. The Netguru team has gone above and beyond any expectations of what a strong and reliable partner can be.
    Hima_Mandali_Solarisbank

    Hima Mandali

    CTO at Solarisbank
  • Working with Netguru has been a fantastic experience. We received a lot of support in terms of thinking about how we track metrics, how we design this properly, and how we build the architecture. We are extremely grateful for making our platform what it is today.
    Manon_Roux

    Manon Roux

    Founder at Countr

Netguru in numbers

  • 14+

    Years on the market

  • 900+

    People on Board

  • 1000+

    Projects Delivered

  • 67

    Our Current NPS Score

Delivered by Netguru


We are actively boosting our international footprint across various industries such as banking, healthcare, real estate, e-commerce, travel, and more. We deliver products to such brands as solarisBank, PAYBACK, DAMAC, Volkswagen, Babbel, Santander, Keller Williams, and Hive.
  • Lead generation tool that helps travelers to make bookings

    $47M Granted in funding

  • Data-driven SME lending platform provider

    $20M Granted in funding

  • Investment platform that enable to invest in private equity funds

    $28M Granted in funding

  • Self-care mobile app that lets users practice gratitude

    $5M Granted in funding

Any computer vision queries?


We’re here to answer questions about CV and how it’s used
In what industries computer vision can be used?

Where can’t computer vision be used is the better question. Wherever humans use their sight for performing a task, computer vision and augmented reality may come in handy.

Just a handful of CV applications include medical image processing, facial recognition (Snapchat, Instagram, Facebook), fingerprint and iris recognition, x-ray-based diagnosis, productivity analytics, tumor detection, crop monitoring, automated license plate recognition (ALPR), theft detection, and traffic flow analysis.

Can computer vision be integrated with other solutions?

Yes, computer vision can be a part of a larger monitoring system, with different sensors – for example, in a factory. In that situation, computer vision is capable of integration, as any other IT system. It depends on the use case as to how it should be done (considering appropriate technology stack).

Other solutions include augmented reality and virtual reality. For example, computer vision helps virtual reality via functions such as SfM (structure from motion), SLAM (simultaneous localization and mapping), gaze tracking, and user body tracking. Combined with cameras and sensors, these capabilities help virtual reality platforms identify the location of a user's headset and analyze their environment.

How does computer vision supports retail?

CV helps the retail industry in many ways, from enabling automatic checkouts, analyzing customer footfall, and people counting, to detecting theft, queue detection, and customer retention.

What’s more, our team of expert computer vision developers has helped clients build efficient and cost-effective intelligent video analytics.

Let’s work together

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