Another summer, another edition of our internship on computer vision to be proud of! This time we received well over 100 applications from more than 20 cities including Kyiv, Kharkiv, Lviv, Dnipro, Odesa, Mykolaiv, Vinnytsia, Uzhhorod, Poltava, Kremenchuk, Sumy, Zaporizhzhia, Kryvyi Pih, and Mariupol. What an impressive geography! Only three of the applicants made it to the ‘finals’.
Want to know what stands behind remote photoplethysmography (rPPG) and how to non-invasively monitor vital parameters such as heart rate and respiration, oxygen saturation, and blood pressure using just a phone camera? During the event, our CEO Ievgen Gorovyi will dive into the details of developing a computer vision-based solution for such healthcare application. 📅 Join us on September 18 at 11:00 in Zoom meeting! 🎯 Participation is free by pre-registration 👉🏻 https://cutt.ly/mWT8uv0.
If you want to dig into Computer Vision (CV) but have no idea where to start, this beginner guide is for you. Here we recommend some sources which will come in handy for learning and understanding both the computer vision and deep learning basics.
What is your first thought when you hear about computer vision (CV) in fashion? Or, what is the first thing that pops into your head when you hear about deep learning fashion? Let us guess – online clothing shopping or virtual try-on applications? Well, this might be surprising but deep fashion is not a far future anymore.
Artificial intelligence (AI) and machine learning (ML) are being progressively used across different sectors including healthcare. One of the AI-powered tools is computer vision (CV), the ability to recognize, interpret, and process visual data. Thus, potential applications of computer vision in the medical field are multifold, from image processing and predictive analysis to automated health records. All this enables improving the quality of delivered medical services and the healthcare administration system.
A century ago, the very thought of machines being able to think, make complicated calculations, and come up with effective solutions to pressing problems was more of a figment of science fiction writer’s fantasy rather than a foreseeable reality.
Deep learning (DL) and neural networks are extremely widespread in different computer vision (CV) applications. Indeed, many typical problems (like object recognition or semantic segmentation) are effectively solved by the convolutional neural networks (CNNs). In this article, we are going to discuss how to utilize CNNs on embedded devices.
Computer vision (CV) and machine learning (ML) algorithms solve a tremendous amount of problems. However many businesses often do not understand what hardware to choose for running your favorite neural net or some advanced image and video processing pipelines. With this blog post, we start a series of articles about embedded vision and specific practical things you need to know before making your choice.
Our client’s goal was to enhance various printed media (magazines, posters, banners, etc.) with interactive experience using augmented reality. With AR, certain areas on the reading materials can be overlayed with digital information of a different kind: from videos, images, and 3D models to weather information and buttons that bring additional functionality, etc.
The task of automatic document analysis and recognition is very common in everyday life. Basically, every time when a user needs to automatically parse and recognize some content from a picture captured with a mobile phone/tablet or a scanned document – for example, text, tables, links, etc., automatic document recognition and text analysis come to the stage.