What is computer vision?

Computer vision is the field within AI where we leverage a computer's ability to understand visual data, primarily images and video.

The core of computer vision is extracting relevant information from visual material and converting it into a basis for decision-making or concrete actions. This involves several common tasks, such as:

- Object recognition (e.g., identifying cars in a traffic image)
- Image or video classification and segmentation (e.g., analyzing X-rays and generating automated diagnoses)
- Pattern recognition (e.g., identifying individuals based on facial characteristics or detecting crops from satellite images)
- Object tracking and movement analysis (e.g., automatically monitoring packages in a warehouse)
- Creating 3D models from 2D images or video (e.g., generating 3D models from a series of images or videos for real estate development)
- Video analysis (e.g., detecting suspicious behavior in retail and alerting security personnel accordingly).

If you can think of any image or video material that your organization already spends time (and money) analyzing, you likely have the foundation for a successful computer vision project.

Whatever your thoughts may be, feel free to contact us for a discussion about the possibilities.

What types of computer vision projects do we work on?

At Trueimpact.ai, we handle a wide range of computer vision projects.

We collaborate with several specialized AI computer vision companies, primarily in Southern and Eastern Europe. In most projects, we bring in top experts—either individuals or entire teams—who possess the precise technical competencies needed for the project.

This means we can execute virtually any computer vision project. We ensure that the project scope is both clear and feasible based on your requirements and the available technological possibilities.

A common factor in all our projects is that we take full responsibility for the entire process—from defining the scope to implementation.

What technologies do we use in computer vision projects?

Through our partnerships with specialized individuals and niche companies, we work with a wide range of technologies, selecting the right one based on your organization's needs and the specific project requirements.

Some of the most commonly used technologies include:

OpenCV (Open Source Computer Vision Library) – An open-source C++ library developed specifically for computer vision and image processing. OpenCV offers features such as facial recognition, object tracking, image filtering, and edge detection.
TensorFlow – Google's open-source machine learning framework, containing several pre-trained models (e.g., for object recognition and image classification) and designed for scalability and GPU acceleration.
PyTorch – Meta’s open-source deep learning framework used to train and test advanced neural networks. We use it for research and experimentation in computer vision due to its strong API, which enables rapid prototyping.
YOLO (You Only Look Once) – An object detection algorithm offering high speed and accuracy for real-time analysis. YOLO is widely used in applications such as self-driving cars, surveillance, and drone technology.
AWS Rekognition – A cloud-based service from Amazon Web Services that provides pre-built models for object, facial, and text recognition. No server setup or model training is required, as everything runs in the cloud.

Training custom models vs. using standard models

Before large language models (LLMs) became widely adopted, many computer vision projects relied solely on training models using proprietary data. While this approach can provide high accuracy and control, it is rarely the best solution today due to the high resource demands.

Modern AI models have become so advanced in image and video recognition that it almost always makes sense to use an existing platform. The choice of platform depends on various factors, which we will explore in collaboration with you.

When do computer vision projects fail, and when do they succeed?

AI is still a relatively new field, and many believe that projects should be started with a limited scope and refined as they progress. While this approach can help build AI maturity and foster learning within an organization, it is not how we operate at Trueimpact.ai.

We believe computer vision projects succeed when the business objectives are well-defined and the necessary prerequisites for execution are carefully assessed. That’s why we always conduct a preliminary analysis of computer vision projects, typically including:

Goals and objectives – Based on our experience and in collaboration with you, we develop a business case that quantifies what we aim to achieve and defines success criteria.
Data requirements – In computer vision projects, it is especially important to define what data you already have and its quality. We conduct a gap analysis to estimate the cost of obtaining the necessary data.
Technology selection – We determine the most optimal technology and ensure it aligns with your organization's competencies and ability to maintain and develop the solution further.
Proof of concept – We develop proof of concept for the project’s most critical elements. In computer vision projects, this typically means verifying whether the chosen solution can accurately recognize or classify objects and integrate with existing systems.
Project and resource plan – Once we are confident that the project is feasible and provides sufficient value, we create a detailed project and resource plan. This ensures clarity regarding tasks assigned to us and your team. The plan also includes training and implementation for end users.

Do you have a relevant project?

Fill out the form below, and we will get back to you with relevant insights and our concrete experience in the field.