Computer vision

Computer Vision (CV) is an AI-based visual technology allowing computerized pictures and video recognition and description. Powered by cutting-edge Machine Learning capabilities, customized CV models can work more quickly, efficiently, and accurately than humans identifying faces, locations, and things. Our custom software development company offers a wide range of Computer Vision solutions that perfectly match the unique requirements of our clients.

Use cases

Computer Vision (CV) solutions are widely used in various fields. VITech works with solutions applied in healthcare to allow more exact diagnostics by analyzing medical images and disease screening. We help our clients in manufacturing to detect and classify defects or improve work safety. Our solutions assist by inventory management and self-checkout in retail and manufacturing. Optical character recognition is more efficient with computer vision software and is often implemented for healthcare & banking. We can assist in original content generation and tracking consumer attention and emotions in marketing as well.

Object detection

Intelligent image analysis solutions automate processes by detecting specific objects, defects, and anomalies in images. Deep learning-based visual search and image classification software point out resemblances and dissimilarities with accurate results. Object detection solutions facilitate automated damage assessment for insurance claims, property maintenance, store inventory management, and other processes. Widely used in a spectrum of services around image retrieval or video monitoring.

Image classification

The computerized picture categorization and organization used for image similarity search engines. Medical image analysis with computer vision solutions can significantly boost the patient’s medical diagnosis accuracy and speed up the whole diagnostic process, resulting in better treatment outcomes and better life expectancy.

Image captioning

Image textual description can be generated automatically with a help of computer vision systems and ML-based training programs. The ML- models work with the objects on the image, their characteristics, the actions being performed, and the interaction between the objects.

Image reconstruction

ML-based image reconstruction is driven by data where a training dataset is used to adjust a parametric reconstruction model. Well-trained computer vision with images incorporated in robotics improves quality assurance and operational efficiencies in diagnostic software, resulting in more reliable and cost-effective results.

Face recognition

ML-based applications can be very helpful in the course of automatic identification of a person on a video or digital image. The algorithm has to juxtapose data on the picture with other data to point out faces. Facial recognition systems are often applied in healthcare, manufacturing, HR management, biometric access control for secure image recognition, and so on.

Instance segmentation

This important part of intelligent image analysis includes partitioning a digital picture into several sections. Instance segmentation handles several objects belonging to the one and the same class as distinct individual samples.

Semantic segmentation

Semantic segmentation associates every pixel of an image with a class label such as a person, flower, car, and so on. It treats several objects of the one and the same class as a single entity. Semantic segmentation is often used for detecting objects on an image.

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