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.
Automatic floor segmentation can serve many interesting purposes including mixed reality (MR) applications, interior design, entertainment, computation of available space in a room, or indoor robot navigation. In this project, we have been solving a problem of scene understanding and, in particular, determining which pixels of the image belong to the floor. The problem of floor segmentation is a good example of how the same task can be solved with classical computer vision algorithms or deep learning.
Our task was to develop the algorithm for the automatic road detection in radar images. The challenge was that the radar images are a bit different from the optical ones. In particular, in the case of synthetic aperture radar (SAR), the image formation process is accomplished via coherent processing of the received signals backscattered from the Earth surface. As a result, the multiplicative speckle noise appears in the SAR images.
