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Computer vision solutions for businesses

Vention’s computer vision services are tailored to meet the changing demands of businesses, harnessing innovative algorithms, tools, and techniques. With a team of visionary experts, we possess the knowledge and expertise to develop computer vision solutions that enhance operational efficiency and help you outrun the market.

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Computer vision 101

Computer vision-powered solutions leverage advanced algorithms and predefined rules to interpret visual data effectively. They empower machines to identify objects, patterns, and even faces, becoming an exquisite tool for businesses seeking to streamline operations, enhance precision, and reduce expenses.

The versatility and robustness of these tools empower them to undertake a wide array of tasks, whether by assisting human workers or automating operations across diverse industries. Their applications span from conducting quality assessments to performing data analytics, enabling businesses to make informed decisions, mitigate risks, adhere to regulations, and uphold safety standards. 

What’s more, these innovative solutions are pivotal in optimizing processes, increasing efficiency, and driving productivity in various sectors — from manufacturing and healthcare to retail and security. Through continuous advancements and integration with artificial intelligence, computer vision is revolutionizing how businesses operate, paving the way for a more automated and data-driven future.

Hear from our expert
Popov Andrei
Andrei Papou
AI Team Lead at Vention
Hear from our expert

Our team boasts deep expertise in computer vision consulting, developing, implementing, and integrating computer vision-powered software.

And our team won’t just develop your product; we will create the solution with architecture and features tailored to your business needs, computational resources, and data quality and sensitivity concerns.

With our support, companies gain a clear insight into their computer vision potential and its optimal application. They also learn how to analyze their data more effectively, pick the best-fit models, and create future-proof systems that are easy to scale and maintain.

The best part? We achieve that swiftly and cost-efficiently, allowing you to focus on vital business tasks instead of getting bogged down in complex technology details and spending countless hours on computer vision market analysis.”


Computer vision techniques and algorithms

Computer vision solutions enable systems to extract valuable data from visual materials, including images and videos, which are then applied to execute specific tasks or make informed decisions. The scope of computer vision extends beyond simple recognition, moving into understanding that triggers responses. 

The technology encompasses a range of techniques tailored to address unique challenges and applications.


Use cases


Image processing


Resizing, filtering, edge detection, and color transformation to enhance the image or extract useful information from digital images

Use cases

  • Enhancing image quality, noise removal, and distortion correction

  • Image compression for efficient storage and sharing

  • Color adjustment for improved visualization


  • Gaussian Blur

  • Canny Edge Detector

  • Histogram Equalization

  • Morphological Transformations (Dilation, Erosion, Opening, Closing)

Feature detection and matching


Utilizing specific features within images to perform tasks like image stitching, object or optical character recognition, or tracking

Use cases

  • Augmented reality (AR) with precise digital overlays

  • Panoramic stitching from multiple overlapping images

  • Creating 3D models from images through feature matching


  • SIFT (Scale-Invariant Feature Transform)

  • SURF (Speeded Up Robust Features)

  • ORB (Oriented FAST and Rotated BRIEF)

  • AKAZE (Accelerated-KAZE)

Classification and object recognition


Determining the category of an object in an image or video sequence and recognizing specific objects

Use cases

  • Image retrieval from databases by content.

  • Face recognition

  • Diagnosing diseases using medical imaging (e.g., X-rays, MRIs)


  • Convolutional Neural Networks (CNNs)

  • Support Vector Machines (SVM)

  • K-Nearest Neighbors (K-NN)

Object detection


Locates objects present in an image by placing a bounding box around each object

Use cases

  • Surveillance by detecting objects (people, vehicles)

  • Inventory management through product counting and shelf placement detection

  • Autonomous vehicles that navigate using obstacle and sign detection


  • R-CNN

  • YOLO (You Only Look Once)

  • SSD (Single Shot Detectors)

Semantic segmentation


Allocating a label to each pixel within an image, ensuring that pixels bearing identical labels are classified under the same object group

Use cases

  • Crop health and land use monitoring via segmentation

  • Land use analysis from satellite/aerial images

  • Medical tissue and organ segmentation for analysis or surgery


  • FCN (Fully Convolutional Network)

  • DeepLab (with Atrous Convolution)

  • U-Net

Instance segmentation


Distinguishes between various instances of the same object

Use cases

  • Inventory and customer behavior analysis through item counting

  • Cell or organism identification in microscopy

  • Text and figure segmentation in documents for digital analysis


  • Mask R-CNN

  • YOLACT (You Only Look At Coefficients)

  • SOLO (Segmenting Objects by Locations)

Pose estimation


Detecting the position and orientation of objects or the human body within an image

Use cases

  • Athletes performance enhancement through posture analysis

  • Physical therapy progress monitoring with exercise tracking

  • Gesture-based controls for interactive systems


  • OpenPose

  • AlphaPose

  • DeepCut

Depth perception


Estimating the distance between objects and the camera, using techniques like stereo vision, structured light, or monocular cues

Use cases

  • Robotics navigation through 3D environmental understanding.

  • 3D modeling from 2D images.

  • AR realism enhancement with accurate digital overlays.


  • Stereo Vision

  • Structured Light

  • Monocular Depth Estimation (Dense Depth)

Motion analysis and object tracking


Analyzing the movement of objects across a series of frames and maintaining their identity

Use cases

  • Security tracking via video surveillance

  • Enhanced sports broadcasts with player and object tracking

  • Wildlife monitoring with minimal disturbance


  • Optical Flow

  • Kalman Filtering

  • SORT (Simple Online and Realtime Tracking)

Image generation and reconstruction


Generating new images or reconstructing images from corrupted inputs

Use cases

  • Digital scene creation for entertainment

  • Age modification for faces in images.

  • Image reconstruction for improved medical diagnosis


  • GANs (Generative Adversarial Networks)

  • Autoencoders

  • StyleGAN

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Computer vision use cases across industries


In healthcare, computer vision is a vital tool that aids healthcare professionals in analyzing CT and MRI scans. It also detects various clinical conditions by automating anomaly detection in medical images, tracking blood loss during childbirth, and improving the diagnostic process in digital pathology.

Computer vision is also applied in movement analysis to diagnose neurological and musculoskeletal diseases​​.

Transportation and autonomous vehicles

Computer vision enables the development of intelligent transportation systems, such as self-driving cars, by empowering vehicles to recognize objects and estimate their motion in real time. The technology also goes the extra mile in pedestrian detection, traffic analysis, and parking management, keeping a close watch on the interaction of vehicles and pedestrians.


In the fast-paced retail sector, computer vision transforms how businesses handle inventory, analyze customer foot traffic with heat maps, and tackle loss prevention. It also revolutionizes the shopping experience with virtual try-ons in digital mirrors, personalized outfit recommendations via custom search engines, and advanced theft detection by monitoring unusual behavior.

Drone and aerial imaging

Geospatial analytics organizations employ computer vision to combine satellite imagery, geographic data, and location data. This supports a range of activities from academic research and environmental conservation to disaster response, humanitarian initiatives, and military drone piloting. The result? Streamlined processes that significantly speed up and simplify decision-making.

Sports analysis and entertainment

Computer vision is widely used in sports to track balls, detect scored goals, generate highlights, and analyze the impact of activities on the final result. This technology helps evaluate player performance, analyze game strategies, and enhance broadcast and viewer experiences by automating the creation of highlights and tracking sportsman activities​​.

In the entertainment industry, computer vision boosts augmented reality filters, post-production video editing, and special effects, elevating the user experience of media creators and viewers​​.

Benefits of custom computer vision solutions

Enhanced accuracy and efficiency

Custom computer vision applications are designed to meet the specific needs of a project or organization, meaning they’re optimized for the particular data types and scenarios encountered. This tailored approach ensures maximum effectiveness of task execution​​​​.

Cost savings

Custom solutions may incur higher initial costs than their off-the-shelf counterparts, but offer more precision and efficiency. Plus, manual task automation frees up human resources for more critical, strategic tasks​​​​.

Scalability and flexibility

We design custom solutions with scalability in mind, allowing organizations to start with a smaller deployment and expand as needed. This flexibility also makes it easier to adapt the system to future requirements, whether that involves processing higher volumes of data, performing new tasks, or integrating with other technologies​​​​.

Competitive advantage

Custom computer vision solutions are instrumental in securing a competitive edge in today’s cutthroat market. Tailored products offer unique capabilities, empowering companies to enhance services, customer experiences, and product quality.

Security and privacy

We develop custom solutions with a strong focus on security and privacy, ensuring compliance with relevant standards. This is particularly important in industries like healthcare and finance, where data sensitivity is paramount. Custom systems improve regulatory compliance and offer superior protection against data breaches compared to off-the-shelf solutions.

Seamless integration capabilities

Our team builds custom computer vision systems that effortlessly integrate image and video analysis with existing IT infrastructure and workflows, minimizing disruption and maximizing compatibility. This integration capability ensures smooth data flow between systems, enhances the overall efficiency of operations, and enables more comprehensive analytics and insights.

Computer vision technologies and tools we employ

Where lies a problem, we see the solution. Here’s what we use to nail tasks related to computer vision.

Cloud platforms

AWS ML tools (SageMaker, CodeGuru, Forecast, Comprehend, and more)

Azure Machine Learning

Google AI Platform, Google Cloud AutoML

Programming languages





















MLOps tools



How we navigate key computer vision challenges:



Data quality and quantity


High-quality, annotated datasets are crucial for training computer vision models. Collecting and preparing such datasets can be time-consuming and expensive.


We enhance the diversity of our training sets through data augmentation, which eliminates the need for more real-world data. Our engineers also generate synthetic data to replicate scenarios that are hard to capture in real life.

Model accuracy and generalization


Ensuring computer vision models perform well on the training data and in real-world, unseen scenarios is difficult.


For better generalizability, we use machine learning strategies like transfer learning, adapting a model from one task to another related one. Our rigorous cross-validation techniques and continuous testing on diverse datasets further enhance model robustness.

Computational resource


Training sophisticated computer vision models requires significant computational power, which can be a barrier for smaller organizations.


We choose cloud computing services that offer scalable, on-demand resources for training complex models. We also use edge computing to decentralize and speed up inference tasks, reducing the reliance on centralized computation power.

Real-time processing


Many applications, such as autonomous driving and surveillance, require computer vision models to process and analyze images and videos in real time.


To achieve real-time performance, our teams optimize algorithms for speed and efficiency, use dedicated hardware accelerators like GPUs and TPUs, and simplify models without significantly sacrificing accuracy.

Privacy and ethical concerns


Computer vision applications, especially those involving facial recognition and people tracking, raise significant privacy and ethical concerns.


Our security engineers implement strict data handling and privacy policies, anonymize data whenever possible, and ensure transparency about how and why computer vision technology is used. Plus, we engage with stakeholders and ethicists to navigate ethical considerations in technology deployment.

Integration with existing systems


Integrating computer vision solutions with existing IT infrastructure and workflows can be complex and time-consuming.


We use a modular and API-driven approach to development for smooth integration with current systems, working alongside IT teams and employing middleware to connect computer vision-powered systems with enterprise operations.

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Our best practices for computer vision development

At Vention, we’re committed to employing top-tier practices in developing computer vision-powered solutions, aiming for unparalleled efficiency, accuracy, and performance in our models. Here's how we approach the development lifecycle:


Data collection and annotation

We prioritize the diversity and representativeness of our datasets to ensure the optimal performance of our models in various scenarios. This approach minimizes biases and improves the models’ generalization abilities.

The quality of our annotations is unmatched, upheld by thorough manual reviews to confirm data accuracy for training our models.


Model selection and training

By utilizing pre-trained models, our software engineers accelerate development and boost model performance, especially in data-scarce settings. We rigorously evaluate and validate our models through cross-validation and testing new data to continuously enhance their performance and generalization.


Computational efficiency

We employ methods like model quantization, pruning, and optimal architecture design to hone our models for efficient inference, which is vital for real-time applications. Our team also uses hardware accelerators like GPUs and TPUs in training and inference stages to bolster computational speed.


Deployment and integration

We ensure scalable deployment through cloud platforms and employ containerization to maintain consistency across different environments — all the way from development to production.

Our team also performs comprehensive integration testing to ensure flawless compatibility with existing systems and infrastructure.


Continuous monitoring and updates

Post-deployment,  we consistently monitor system performance to identify and rectify any issues or accuracy declines, ensuring our models are up-to-date and effective.

Keeping abreast of the latest developments in the field is a priority for us. This commitment allows us to constantly refine our applications with current research, methodologies, and tools.


Collaboration and knowledge sharing

By encouraging collaboration across various domains, we ensure our solutions are user-focused and adhere to ethical standards.

Our engineers champion a knowledge-sharing culture, promoting the exchange of insights, challenges, and solutions to drive innovation.

Why us? Because we build the future you envision


Years of experience developing custom solutions


AI industries and 10+ unicorn clients


Engineers with AI-specific skill sets


AI projects successfully finished

Delivery on time, on budget, and on scope

ISO 27001-certified for security management practices

Our AI pros boast notable achievements in Kaggle competitions and projects


Assistance in choosing stacks that reduce both upfront and ongoing maintenance costs

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Still not sure?

Your journey doesn't have to end here. Let's discuss how our expertise can help boost your business.

Success stories

Case study

motum (by RepairFix)


Motum simplifies fleet management and insurance claims by providing businesses with a smart platform that automates and streamlines administrative duties. Vention's software engineers introduced features like image uploading, automated alerts, and integration with an AI-powered tool for detecting car damage. Additionally, our QA engineers guaranteed the smooth operation of the solution.




A veteran in photogrammetry software, Vexcel is one of our long-term clients, with a wide range of solutions developed in tandem during our partnership. We’ve created a framework for batch processing, a handful of ML-based aerial image analysis tools, and in-browser map applications that serve as the frontend of the algorithms running in the background.

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