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.
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.
The goal was to develop the approach for the automatic detection of the number plate area from the vehicle image. A typical ANPR algorithm includes several steps (Fig. 1): The number plate detection is a crucial step since it strongly affects on the overall system performance.
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.