Computer Vision Developer

Computer Vision Developer

We are looking for an experienced and dedicated software engineer with a strong mathematical background, ability to solve challenging technical problems in image processing and computer vision. Key responsibilities are development of image and signal processing algorithms, reading and implementation of state-of-the-art papers, R&D activities. Main research directions are pattern recognition, object tracking, detection and recognition.

Deep Learning Engineer

Deep Learning Engineer

We are looking for an experienced and dedicated data scientist with a strong mathematical background, ability to solve challenging technical problems using machine learning and deep learning in image and video processing domains.

Mobile Indoor Navigation: From Research to Production

Indoor positioning systems in GPS-denied environments are rapidly becoming popular. Various options are commonly available (BLE, Wi-Fi, ultra-wideband, ultrasonic, etc.). The key challenge is to provide accurate, and stable real-time user location at a low cost. In this paper, we present the research and production details of the developed hybrid indoor localization and navigation system (HILN).

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Automatic document analysis and recognition is a hot topic in a modern computer vision. A common scenario is when the user takes a picture by mobile phone or tablet and the goal is to automatically parse and recognize content from the captured document. Such like pictures, tables, text data, links, etc.

Planar Tracker for Augmented Reality

Planar Tracker for Augmented Reality

Marker-based augmented reality (AR) is extremely popular nowadays. However, full user immersion is possible only in the case of robust real-time computer vision solutions working on the mobile device. We have developed a custom hybrid tracking system based on local feature tracking and template-based matching.

Object Recognition in Radar Images

Object Recognition in Radar Images

Object recognition is an important computer vision and machine learning problem. A particular case is automatic target recognition (ATR) on radar images. In the project, our team has developed a custom classification algorithm based on two different tools. We have been working with MSTAR dataset.

Binary Marker Recognition on Raspberry

Binary Marker Recognition on Raspberry

Fiducial markers are widely used in various applications like robot navigation, logistics, augmented reality. Fig. 1. Applications of fiducial markers Advantages are obvious High contrast Simple code generation Resistance to extremal angles However, when we deal with a large number of markers, real-time recognition becomes challenging, especially on embedded devices with low power CPUs on-board.

Indoor Positioning Engine

Indoor Positioning Engine

Indoor positioning systems are becoming popular nowadays. Indeed, there is plenty of opportunities for real-time user navigation in GPS-denied environments. An interesting use cases are as follows: Fig. 1. Indoor navigation use cases There are several options for hardware (see It-Jim blog post).