Computer Vision Development

Computer Vision Development

Look deeper and see more with the potential of Computer Vision.

Computer Vision

Computer vision is one of the areas of Artificial Intelligence that works with computers and systems that receive information from video, photos, and other visual data. Artificial intelligence is the brain of the system, and computer vision is its eyes.

There are endless choices of computer vision examples, which show all the benefits and create new scaling opportunities for companies. CV has had active usage for years and we can see how it works and the result of the technology’s implementation.

Human Vision vs Computer Vision

Human vision system

Human vision system

computer vision system

computer vision system

Quick statistic

According to the Statista publication, it is assumed that:

  • The size of the computer vision market will reach 22.27 billion USD in 2023.
  • The market size will show an annual growth rate (CAGR 2023-2030) of 12.56%, which will lead to the fact that the market volume by 2030 will be 50.97 billion US dollars.

We are experienced in such expertise

Market Size 2030
Computer Vision Expertise

Image Recognition

You can use Image Recognition in your business if you need to detect and analyze images that can automate certain tasks. This technology’s functions include categorizing image to specific pre-defined groups of classes or types of elements characteristic of images. On their basis and analysis, image recognition makes certain conclusions to solve various tasks.

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Text Recognition or OCR

If you need a solution that recognizes text on the image, Amazinum Company is ready to perform it. Text Recognition or OCR refers to the field of computer vision that is responsible for reading text in images, recognizing characters – and thus identifying text. Ultimately, it is converted into a machine-readable form that can be used for data processing.

Text Recognition or OCR

Object Detection

It is very simple to be a leader among competitors by simply applying Machine Learning algorithms. And the Amazinum team is ready to help you with this. Object detection is,  in most cases used to detect the location of objects in video and images. Object detection uses many methods to achieve its tasks, including image processing, pattern recognition, artificial intelligence, and machine learning.

Object Detection

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Semantic Segmentation

Semantic segmentation is a computer vision technique that analyzes and classifies an image at the pixel level in order to identify objects on it. The technique tries to determine the category of each pixel in the image. This method requires a combination and an increase in resolution since its application requires a lot of resources. That is, semantic segmentation classifies similar objects as a single class at the pixel level.

Semantic Segmentation

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Instance Segmentation

Instance segmentation is an advanced technique that classifies objects in images at the pixel level and identifies each instance of the object. Instance segmentation classifies similar types of objects into different categories. This computer vision technique uses Mask R-CNN structures, which helps it distinguish between instances within classes. 

Instance segmentation requires analyzing the difference between visual data with different overlapping objects and different backgrounds. Our specialists use CNN (convolutional neural networks) or ViT (Visual Transformers) for the segmentation of instances. Thanks to this, they find objects at the pixel level.

Instance Segmentation

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Object tracking

Object tracking is a technique used to track objects in an image or video after they have been detected. Thus, after determining the starting position, the computer in online or offline mode can predict the drift in each subsequent frame.

There are two types of object tracking:

  • Single object tracking
  • Track multiple objects

Data scientists use two methods to track objects:

  • Generative – by which the system identifies the object by its distinguishing features
  • Discriminative – by which computer vision is used to distinguish the object from the background.
track objects

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Face and Person Recognition

Face recognition is a part of object detection, but the main object here is the human face. The algorithm looks for common features and relies on unique points, such as eyes, lips or nose, and classifies the face based on these landmarks.

There are different methods to deal with this type of task. One of the traditional methods of image processing for face recognition is the Haar cascades algorithm. On the other side, deep learning algorithms using CNN networks show more reliable and efficient results.

Face and Person Recognition

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Business value:

  • Computer vision has the ability to increase productivity across a range of industries by automating repetitive tasks and enhancing accuracy.
  • A vast array of tasks that previously required human intervention can be automated by computer vision, including inventory management, security surveillance, and quality control in manufacturing.
  • Solutions based on computer vision will help you to make supplies more efficient, track stock levels, and identify product defects or delays in the production process. In this way, you will increase the enterprise’s productivity, reduce waste, and increase your profit.
  • Computer vision has also shown its high efficiency in detecting fraud. 
By analyzing data from security cameras and transaction records, it can identify potential fraudsters and stop fraudulent activity by identifying trends and anomalies in the data.
  • Companies can use computer vision to customize their goods and services according to the tastes and actions of their customers. Both the customer experience and customer loyalty may benefit from this.

Computer Vision & Business Solutions

Computer Vision Applications

Augment reality (AR)

imaging (SRI)

Optical character 
recognition (OCR)

Self-driving vehicles

Facial recognition


Let Amazinum Data Scientists develop solutions for you that will improve your business and lead to success. The potential of computer vision affects many areas. Expand your possibilities with Amazinum.


Cashless stores, automated scales, and product absence sensors all optimize the work of staff and allow them to spend more time with customers. E-commerce has used customers’ previous experiences to create personalized offers, thereby encouraging them to buy more. And these are far from all the cases that you can solve with the help of Computer Vision

Education Sector

The impressive capabilities of computer vision are opening up in the field of education Computer Vision can recognize responses written by students and also assist with their automatic evaluation, understand behavior, and improve content based on students’ previous experiences, automating classroom monitoring to deter cheating in tests is already used in many cases, especially when we talk about distance learning. Computer Vision-powered webcams identify students’ cheating behaviors by tracking their postures or eye movements.

Healthcare Transformation

Сomputer vision technology helps practitioners interpret X-rays, CT scans, MRIs, and microscopic images of cellular structures more accurately when diagnosing breast, brain, lung, or skin cancer. Surgical modeling and 3D visualization in the operating room significantly help surgeons and contribute to increasing surgical precision. Rehabilitation applications that use Computer Vision give the ability to stay home and exercise in the right way with the doctor’s advice directly.

Fitness and Sport

You can achieve more results and better understand each player simply by implementing performance control, intensity adjustment, and comparison of achieved results. In addition, you can track the players and the ball on the field so that the referee can make accurate statistical conclusions.


Old-fashioned methods and equipment are gradually disappearing from agricultural areas across the globe. Computer vision is being used by farmers nowadays to increase agricultural productivity. Advanced computer vision and artificial intelligence models are being developed for sowing and harvesting purposes by agriculture technology companies. These solutions can also be used for advanced weather analysis, weeding, and plant health detection.

Numerous current and future agricultural applications of computer vision include yield tracking, autonomous pesticide spraying, drone-based crop monitoring, intelligent crop sorting and classification, and automated pesticide spraying. For additional analysis, these AI-powered solutions scan the shape, color, and texture of the crops. Weather records, forestry data, and field security are also being used more and more through computer vision technology.


Manufacturing is one of the most technology-intensive processes in the modern world. Computer vision is popular in manufacturing plants and is commonly used in AI-powered inspection systems. Such systems are prevalent in R&D laboratories and warehouses and enable these facilities to operate more intelligently and effectively. For instance, predictive maintenance systems use computer vision in their inspection systems. These tools minimize machinery breakdowns and product deformities by constantly scanning the environment. If a likely breakdown or low-quality product is detected, the system notifies human personnel, allowing them to trigger further actions. Apart from this, computer vision is used by workers in packaging and quality monitoring activities.


The COVID-19 pandemic has given remote education a boost, and as a result, the education technology sector is utilizing computer vision for a range of purposes. For example, educators use computer vision tools to assess student learning in a non-obstructive manner. With the use of these tools, educators can spot disengaged pupils and modify their instruction to make sure they stay caught up.

Aside from this, AI vision is being used for tasks like regular assessments, knowledge acquisition, attendance tracking, and logistical support for schools. Computer vision-enabled webcams, which are used to monitor students during exams, are one common example of this. This facilitates the detection of unfair practices through the examination of body language and eye movements.


These days, computer vision is used in interactive entertainment systems to create incredibly immersive experiences. Artificial intelligence is used by cutting-edge entertainment services to give users access to dynamic experiences.

As an illustration of how users can obtain information about what they see while looking at it, consider Google Glass and other smart eyewear. The user’s field of vision receives the information directly. Users can communicate commands simply by moving their heads thanks to these devices’ ability to react to head movements and facial expression changes.


Computer vision has a lot to offer the financial services industry, particularly when it comes to helping with digital transformation. The following areas are where banks and other financial institutions should anticipate improvements:

  • Document digitization: for quicker data extraction and processing
  • Face recognition: an improved level of security and Know Your Customer support
  • Damage assessment: for insurance providers, covering real estate and automobiles

Banks can use computer vision technology to generate a wealth of useful data that will help them optimize their operations and enhance customer satisfaction.

Companies that already use Computer Vision


Bosch uses computer vision in industrial automation, driver assistance systems, and video surveillance.


Using computer vision, Pinterest facilitates visual search for objects by letting users take pictures or pick specific areas of images to search for.

Zebra Medical Vision

Based on user ratings, viewing history, and preferences, Hulu uses recommendation algorithms to offer TV series and movies to users.


Affectiva analyzes facial expressions using computer vision and emotion recognition technologies to enable emotion-aware AI applications in fields like user experience testing and market research.

Waymo (a Google/Alphabet subsidiary)

Waymo is a self-driving technology company that uses computer vision to enable its driverless vehicles to see and understand their surroundings.


CureMetrix improves the precision of medical imaging diagnostics by utilizing computer vision in mammography to detect breast cancer.

Obsidian Security

Obsidian Security offers threat detection and response solutions for cybersecurity using computer vision and machine learning.


Aibee specializes in artificial intelligence (AI) solutions, encompassing computer vision, for uses in retail analytics, smart city technologies, and facial recognition.

SenseTime icon


SenseTime is a top artificial intelligence company that uses computer vision to solve problems with smart cities, driverless cars, and facial recognition.


What is the difference between image recognition and computer vision?
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More precisely, computer vision refers to a collection of methods that make it possible to automate processes using a stream of images or videos. Computer vision is a subset of image recognition. It consists of a collection of methods for identifying, evaluating, and interpreting pictures to support judgment calls.

How to implement computer Vision?
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  • OpenCV. OpenCV (Open Source Computer Vision Library) is one of the most widely used computer vision libraries.
  • Viso Suite.
  • TensorFlow.
  • CUDA.
Which tools are best for Computer Vision?
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  • Facial recognition.
  • Self-driving cars.
  • Robotic automation.
  • Medical anomaly detection.
  • Sports performance analysis.
  • Manufacturing fault detection.
  • Agricultural monitoring.
  • Plant species classification.

Amazinum Portfolio

Check out a few of our recent projects

Smart Eyes: The Integration of AI and ML in Surveillance Cameras for Enhanced Security

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Portfolio graphics
Machine Learning in Retail: Grocery Product Recognition in Hypermarkets

Beyond Monitoring: The Power of AI and ML in Construction Site Surveillance

Our Industry Focus

Our industry knowledge and background give our clients and partners confidence that we understand their business. Here we highlighted a few top industries we are good at, penetrating to the smallest details and nuances of a certain branch.

SEO & Advertising icon

SEO & Marketing


Safety & Security

Sport icon


E-commerce & Retail icon

E-commerce & Retail

Gambling and Casino icon

Gambling and Casino

Manufacture icon


Ecology icon



Energy icon


Localisation icon


IoT icon


Partners & Clients

We deeply appreciate our partners for cooperation. Every member of the Amazinum Team does their best to provide the highest quality of services and solutions. We satisfy all your needs and requirements.

byteant logo
sleepnumber logo
softserve logo
aid genomics logo
Vimmi logo
PVH logo
sloboda studio logo
AstonUniversity logo
YellowMedia logo


Learn about technology stack we use to implement data science:

Programming languages:

Python logo

for data analysis and processing:

OpenCV logo
NumPy logo
SciPy logo
Pandas logo

for creating solutions:

spaCy logo
Spark logo
PyTorch logo
TensorFlow logo
Keras logo
Scikit-Learn logo

for API development:

RabbitMQ logo
Flask logo
FastAPI logo
Django logo

for visualization:

ploty logo
matplotlib logo

Deploying solutions:

Docker logo
GitLab logo
TensorFlow logo
DVC logo logo
KubeFlow logo



PostgreSQL logo
ClickHouse logo
BigQuery logo
MySQL logo


Bigtable logo
Elastic logo
mongo DB logo
Cassandra logo

Cloud Solutions:

Google Cloud logo
AWS logo

Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

Vitaliy Fedorovych contact us photo

Hello there!

Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together

Contact Us

Click or drag a file to this area to upload.
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Bazarnitska Alina

Client Partner

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Ihor Khreptyk

Client Partner

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Stopnyk Zoriana

Client Partner

Vitaliy Fedorovych contact us photo

Vitaliy Fedorovych

CEO, Data Scientist

Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

Vitaliy Fedorovych contact us photo

Hello there!

Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together

Contact Us

Click or drag a file to this area to upload.

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