Year: 2017

Advanced Image Tracking Approach for Augmented Reality Applications

Augmented reality is popular and rapidly growing direction. It is successfully used in medicine, education, engineering and entertainment. In the paper, basic principles of typical augmented reality system are described. An efficient hybrid visual tracking algorithm is proposed. The approach is based on combining of the optical flow technique with direct tracking methods. It is demonstrated that developed technique allows to achieve stable and precise results. Comparative experimental results are included.

Framework for Real-Time User Positioning in GPS Denied Environments

In the paper, a system for real-time positioning is proposed. Developed signal processing algorithms for precise user localization and navigation are described. It is demonstrated that proper calibration and received signal filtering leads to improvement of positioning accuracy. Peculiarities of Bluetooth Low Energy beacons as signal sources are considered. Key components of the created software development kit are described. Experimental results of testing on mobile platforms are given.

Biological Cells Segmentation
Biological Cells Segmentation

The task of accurate cell segmentation is essential for cellular biology and single-cell analysis, as well as for studying biological processes as a whole. In biomedical image processing, this includes reconstruction of microscopy images, foreground segmentation, cell detection, cellular compartments and organelles segmentation. Despite the tremendous progress in microscopy cell imaging and numerous segmentation methods, accurate segmentation and reliable characterization of cells remain challenging tasks that usually require problem-specific tailoring of algorithms.

Comparative Analysis of Convolutional Neural Networks and Support Vector Machines for Automatic Target Recognition

Nowadays automatic methods based on artificial intelligence are rapidly growing. In the paper, a problem of automatic target recognition in synthetic aperture radar images is described. It is demonstrated, that two different machine learning instruments can provide very high classification accuracy. In particular, support vector machines with proper optimization and developed local feature set gives competitive results. Secondly, a novel architecture of convolutional neural network is proposed. Important practical aspects of both methods are analyzed.

Overview of Indoor Navigation Technologies
Overview of Indoor Navigation Technologies

The development of indoor navigation services and algorithms is becoming a popular trend in the IT industry in recent years. Some of the modern buildings, like airports, shopping malls, and warehouses have grown enough (Fig.1) to feel a need for their own navigation tools for customers. Closed environment conditions exclude the usage of common satellite-based navigation systems like GPS or GLONASS, so nowadays some alternative information sources of user localization appear at the scene. Fig.1.

Efficient Object Classification and Recognition in SAR Imagery

SAR is a very popular instrument for imaging of the ground surface. Possibility of high-resolution image formation makes it superior tool for various information extraction tasks. In the paper, a problem of automatic target recognition is comprehensively analyzed. An idea of azimuth and range target profiles fusion is proposed.

Real-Time System for Indoor User Localization and Navigation using Bluetooth Beacons

Real-time user positioning and navigation services are widely used daily by millions of people. The challenge is that common global positioning systems fail in indoor environment or in scenes with a limited sky view. In the paper, the indoor navigation framework based on the Bluetooth beacons is proposed. Such system allows user to obtain the position in GPS denied environment on a real-time basis.

Pattern Detection and Recognition in SAR Images

Synthetic aperture radar (SAR) is a powerful tool for remote sensing of the Earth surface. In the paper, several applications of pattern detection and recognition algorithms for extraction of information from SAR images are discussed. In particular, an idea of usage of optical flow techniques for automatic estimation of the moving target displacements from a sequence of single-look SAR images is proposed. It is shown, how this technique can be adopted for SAR imagery.