The scientific field of computer vision studies how to extract, analyze, and understand information from images and video.
We humans use our eyes and brains to see and understand the world. Computer vision is a technology that provides machines with these same capabilities. The ability to understand the real-world environment is the key to automation processes that currently require human engagement.
Machines that can gain high-level understanding of the real world open up incredible opportunities for a wide diversity of businesses. Among the benefits of integrating computer vision technology into existing systems are improved speed of mechanical processes, reduced operational costs, and increased security. Moreover, computer vision reveals new areas and allows enterprises to create new innovative products and services.
What We Do
Our R&D team is constantly adapting knowledge in the computer vision field, evaluating and testing new approaches and methods, taking on new client challenges and solving new problems.
Image and Video Pre-Processing
In most cases, captured images suffer from distortions, noise, and insufficient contrast. Pre-processing techniques are used to prepare images for further high-level methods. This preparation includes skew and rotation elimination, noise reduction, up- or downscaling, aspect-ratio restriction, color, contrast and sharpness adjustments.
Object and Event Detection
Event and object detection is based on discovering moving a region by subtracting the current image from a reference background. This relatively simple and robust technique allows detection of all moving objects, extraction of information about their size and color, and recording the path of each object. The selected object is often passed to the next process as an individual image.
High-Level Processing
High-level processing includes complex algorithms that allow the machine to recognize the content of an image and tell you what it sees. The computer performs precise object classifying, extracting object features, estimating size and positioning. This group of methods is used in very different areas, such as
robotics, yield sorting, and face recognition (BitRefine Heads platform)
Scene Segmentation
Scene segmentation methods not only allow recognition of objects, but recovery of a 2D outline of each object. This group of methods is computationally intensive and, in most cases, based on de-convolution neural networks. Autonomous navigation, robotics, radiology – all such areas extensively use scene segmentation methods.
3D Scene Reconstruction
Although we live in a three-dimensional world, visual information typically comes in the form of 2D images. 3D reconstruction is a computer vision method that recovers the underlying 3D model from a series of 2D projections of real-world objects such as sequences of aerial photos, interior photos, MRI or X-Ray images. The result comes in the form of an intuitive 3D object.
Video and Image Content Indexing
Visual content indexing is an interdisciplinary area that uses computer vision methods to extract the content of images and make quick search possible among millions of images without prior manual tagging.
BitRefine Group offers universal flexible tool for deep visual analysis to the companies that work in automation, medical, security and many other industries. This tool is a new generation of computer vision software that combine traditional recognition algorithms with almost infinite capabilities of artificial “brain” based on deep neural networks. Name of the tool is “BitRefine Heads” and its main purpose is to allow companies automating process of evaluating even the most complex visual ...
Computer vision has transformed the ways in which machines help humans. The British Machine Vision Association and Society for Pattern Recognition (BMVA) defines computer vision as “the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images” which helps to achieve automatic visual understanding.
Jonathan Swift, the author of Gulliver’s Travels, once said, “Vision is the art of seeing what is invisible to others”. His words are true even today and are applicable to computer vision technology. In order to develop computer vision, neural networks are built. Neural networks allow us to build AI object recognition systems that work better than human. Effectively, machines can ‘see’ images and objects in large datasets that were lost in large amounts of data.
Today, manufacturing is becoming more complex, as well as more automated. The industrial Internet of Things is generating great volumes of data at incredible speed, forming foundation of big data for manufacturing industry. Our R&D team works on a number of solutions that use modern computer vision and machine-learning techniques to increase speed of manufacturing processes, improve reliability, and make forecasting models based on sophisticated data analysis.
The transportation industry covers air, roads, railroads, and ocean lines. The future of transportation is increasingly reliant on advances in computer vision and smart systems. Our team works on a number of applications in the transportation industry that employ computer vision and machine learning technologies. We are excited about the fact that our expertise helps in the development of smart transportation. Our smart solutions bring a new level of comfort and improved travel safety to ordinary people, as well as reduce costs for companies and authorities in charge of transport infrastructure.
The retail industry has learned to collect a vast amount of data: sales, prices, costs, logistical data, product-related data, consumer behavior and much more. All this is available for retailers now. The next step is to transform this data into valuable insight. Powerful tools like machine-learning technologies open up a new world that allows retailers to see beyond collected data. The ability to refine, understand and act upon key factors revolutionize how business is done and brings the whole industry to a new level.
Traditional agriculture practices entail common tasks such as soil preparation, planting, water and nutrient management, weeding, harvesting, and sorting. Today with the advent of modern technologies, most processes can be successfully automated. From a technological point of view, computer vision and machine learning play a central role. For example, smart systems can sort yield, visually examining each single fruit as a human would do, but with incredible speed. Intelligent weeding systems recognize weeds by their shape and allow their elimination chemically or mechanically. Farm vehicles equipped with a computer vision navigation system can work in the field autonomously—in other words, with no driver. In the end, all these systems enable farmers to significantly reduce costs and increase productivity at the same time.
The finance and insurance industry has been always one of the most intensively data-driven industries. With a large amount of diverse data and high demand on data analytics, the finance and insurance sectors have devoted attention to the rapidly developing area of machine learning. Today, machine learning offers the best opportunities in big data analysis. Using ML, finance institutions are able to reveal hidden dependencies and specified patterns among millions of records in a fraction of a second, detecting fraud, for example, or recognizing a trend.
Fortunately, these days we see continuous development of diagnostic and treatment tools available in hospitals and clinics. High-definition scanners allow radiologists to recognize anomalies more reliably and efficiently. However, the healthcare industry is always facing challenges. Our group works on solutions that help the whole industry take a big step forward: we work on automating the process of detecting the anomaly itself. Modern computer vision together with deep-learning models is already capable of seeing objects on radiology images and marking them out automatically. Such computer-aided diagnosis systems help doctors in analysis of medical images, increasing reliability and reducing workload.
Life sciences like other sciences heavily depend on routines such as counting, tracking, and classification. Today, many of these tasks can be successfully transferred to machine-learning and computer-vision systems. However, different studies and different objects either require adjustment of existing machine-learning models or tailor-developed solutions. BitRefine group offers tailor-made algorithms and is proud to take part in scientific research.
We Use
Our engineers’ broad experience allows us to carry out complex tasks, offering clients flexible, high-performance, end-to end solutions.
