Ilya Sochenkov

Face recognition based on a matching algorithm with recursive calculation of oriented gradient histograms

A face recognition method based on a matching algorithm with recursive calculation of oriented gradient histograms for several circular sliding windows and a pyramidal image decomposition is proposed. The algorithm produces good results for geometrically distorted and scaled images.

A fusion algorithm for building three-dimensional maps

Recently various algorithms for building of three-dimensional maps of indoor environments have been proposed. In this work we use a Kinect camera that captures RGB images along with depth information for building three-dimensional dense maps of indoor environments. Commonly mapping systems consist of three components; that is, first, spatial alignment of consecutive data frames; second, detection of loop-closures, and finally, globally consistent alignment of the data sequence. It is known that three-dimensional point clouds are well suited for frame-to-frame alignment and for three-dimensional dense reconstruction without the use of valuable visual RGB information. A new fusion algorithm combining visual features and depth information for loop-closure detection followed by pose optimization to build global consistent maps is proposed. The performance of the proposed system in real indoor environments is presented and discussed.

Exactus Like: Plagiarism Detection in Scientific Texts

The paper presents an overview of Exactus Like – a plagiarism detection system. Deep parsing for text alignment helps the system to find moderate forms of disguised plagiarism. The features of the system and its advantages are discussed. We describe the architecture of the system and present its performance.

Information Retrieval for R&D Support

Research and development (R&D) involves not only researchers but also many other specialists from different areas. All of them solve a variety of tasks that require comprehensive information and analytical support. This chapter discusses the major tasks arising in R&D: study of the state of the art in a given research area, prospects assessment of research fields and forecasting their development, quality assessment of scientific publications including plagiarism detection, and automated examination of proposed R&D projects. A number of informational and analytical systems have been developed to address these tasks. The main goal of this chapter is to give a review of R&D support functions of well-known and widely-used search and analytical systems and discuss information retrieval methods behind these functions.

Social Tension Detection and Intention Recognition Using Natural Language Semantic Analysis: On the Material of Russian-Speaking Social Networks and Web Forums

The paper proposes a method of social tension detection and intention recognition based on natural language analysis of social networks, forums, blogs and news comments. Our approach combines natural language syntax and semantics analysis with statistical processing to identify possible indicators of social tension. The universal components of our method incorporate the general laws of natural language, general psychological, sociological and psycholinguistic rules and trends typical of social tension detection in virtual discussions. Automatic monitoring of the contents of discussions helps to timely unveil hidden signs of tension and makes it possible to predict the likely development of the situation.

Method for Pornography Filtering in the WEB Based onAutomatic Classification and Natural Language Processing

The paper presents a method for pornography detection in the web pages based on natural language processing. The described classification method uses feature set of single words and groups of words. Syntax analysis is performed to extract collocations. A modification of TF-IDF is used to weight terms. An evaluation and comparison of quality and performance of classification are given.

Training Datasets Collection and Evaluation of Feature Selection Methods for Web Content Filtering

This paper focuses on the main aspects of development of a qualitative system for dynamic content filtering. These aspects include collection of meaningful training data and the feature selection techniques. The Web changes rapidly so the classifier needs to be regularly re-trained. The problem of training data collection is treated as a special case of the focused crawling. A simple and easy-to-tune technique was proposed, implemented and tested. The proposed feature selection technique tends to minimize the feature set size without loss of accuracy and to consider interlinked nature of the Web. This is essential to make a content filtering solution fast and non-burdensome for end users, especially when content filtering is performed using a restricted hardware. Evaluation and comparison of various classifiers and techniques are provided.

An Information Retrieval System for Decision Support: An Arctic-Related Mass Media Case Study

This paper discusses the problem of building a comprehensive information retrieval system that facilitates the decision-making process in a specified wide topic. We analyze the requirements for such a system, types of information sources, and typical search queries and propose an architecture and an integrated pipeline. We also present a case study in the field of Arctic exploration (oil & mining, ecology issues, etc.). The results are also presented, including vibrant topics and typical associations between entities.

Convolutional neural networks and face recognition task

Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

Robot mapping algorithm based on Kalman filtering and symbolic tags

A new method will be developed in the present work of the detection of a robots position in a relative coordinate system based on a history of camera positions and the robots movement, symbolic tags and on combining obtained three-dimensional depth maps that account for accuracy of their superimposition and geometric relationships between various images of the same scene. It is expected that this approach will enable one to develop a fast and accurate algorithm for localization in unknown dynamic environment.