In the paper, we analyze the problem of automatic room floor segmentation. For this purpose, we consider several classic computer vision algorithms as well as some of the deep convolutional neural network architectures. The segmentation results are illustrated and compared. An idea for combining two groups of methods is proposed. It is demonstrated that a proper fusion provides the best segmentation quality.
Data Science UA will gather participants from all over the world at the 9th Data Science UA Conference which will be held online on November 20th, 2020. The conference will last for 24 hours non-stop consisting of three significant tracks: Technical track, Workshops track, and Business track.
Meet our team at the 17th International Radar Symposium in Krakow! We are presenting “Frame-Based SAR Processing and Automatic Moving Targets Parameters Extraction” paper there.
Meet our team at the 18th International Radar Symposium in Prague! We are presenting Efficient Object Classification and Recognition in SAR Imagery paper there.
Meet our team at the Signal Processing Symposium – 2017 in Poland! We have two presentations there: Advanced Image Tracking Approach for Augmented Reality Applications Framework for Real-Time User Positioning in GPS Denied Environments
The European Conference on Computer Vision (ECCV) is one of the top computer vision conferences in the world. In 2018, it is to be held in Munich, Germany. There were 2439 paper submissions, of which 776 were accepted (59 orals, 717 posters).
It-Jim’s CTO Pavlo Vyplavin will provide some insights on the tasks we solve at our company at the Introduction to Machine Learning (for scientists) workshop organized by EPS Kharkiv Young Minds Section and Young Scientists Council of O.Ya. Usikov Institute for Radiophysics and Electronics. The second day of the workshop will be devoted to practical implementation of the concepts from the theoretical lecture.
The paper presents basic concepts of augmented reality applications and challenges in building them in the web. We describe the technical and algorithmic stack required to develop, implement and deploy the augmented reality application. Theoretical concepts behind marker detection and tracking are discussed. Two different pipelines are implemented: server-based with algorithms execution in the cloud and completely front-end solution that runs on a user device.
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.
In the paper, we describe the technical details of a multi-player sports tracker system. We demonstrate that proper in-depth analysis of video frames sequence may provide a lot of useful information required for sports analytics. Object detection and tracking steps are analyzed. Novel ideas for efficient filtering of false detections and irrelevant tracks are proposed.