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.
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. The tracking engine tracks the homography changes using optical flow algorithm and then refines the residual warp using the optimized template matcher.
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. This is a public dataset containing ten classes of vehicles with different orientations with 0.3mx0.3m.