Vladislav Myasnikov

Correcting color and hyperspectral images with identification of distortion model

Abstract This paper presents a novel identification-based image correction method using a bi-illuminant dichromatic reflection model. Image patches with uniform properties over distorted and distortion-free images or image parts are used as a prior knowledge for identification. We identify the distortion correction function on a set of these patches, called spectrum shape elements, with the Hausdorff metric. The main issue during prior knowledge representation is for each distorted spectrum shape element to find a corresponding distortion-free element. A necessary condition to find a matching spectrum shape element is presented and theoretically proved. Identification problem was solved using a RANSAC-based optimization with this necessary condition as an optimization constraint. The method works well both for color and hyperspectral images. The proposed image correction procedure was tested on a set of color images and AVIRIS hyperspectral remote sensing data and proved to provide the quality superior to the results obtained with Retinex correction.

Application of polynomial bases for image processing using sliding window

The work deals with the application of polynomial bases for digital image processing using sliding window. An algorithm is built for the parallel-recursive calculation of local moment characteristics. A parametric set of polynomial bases is introduced that yields the fastest realization of the algorithm. We consider methods of the calculation of the polynomial approximation parameters for the convolution kernel. The examples are adduced of the employment of polynomial bases for the 2-D signal filtration, and for the detection and recognition of objects on the image.

Constructing efficient linear local features in image processing and analysis problems

We give formalize the problem of creating single and sets of linear local features for digital signals and images. A linear local feature is a pair consisting of a finite impulse characteristics (FIC) and an algorithm for computing the linear convolution of the signal with that FIC. Efficient linear local features should show optimal behavior: the algorithm should have minimal computational complexity, and the FIC should best accommodate the quality criterion for a specific applied problem. We offer formulations for the problems of creating single features and sets of efficient linear local features, where the process of computing features is connected with sequences of a special kind. We give examples of such sequences.

A Copy-Move Detection Algorithm Using Binary Gradient Contours

Nowadays copy-move attack is one of the most obvious ways of digital image forgery in order to hide the information contained in images. Copy-move process consists of copying the fragment from one place of an image, changing it and pasting it to another place of the same image. However, only a few existing studies reached high detection accuracy for a narrow range of transform parameters. In this paper, we propose a copy-move detection algorithm that uses features based on binary gradient contours that are robust to contrast enhancement, additive noise and JPEG compression. The proposed solution showed high detection accuracy and the results are supported by conducted experiments for wide ranges of transform parameters. A comparison of features based on binary gradient contours and based on various forms of local binary patterns showed a significant 20–30 % difference in detection accuracy, corresponding to an improvement with the proposed solution.

Atmospheric correction of hyperspectral images based on approximate solution of transmittance equation

The paper presents a method of atmospheric correction of remote sensing hyperspectral images. The method based on approximate solution of MODTRAN transmittance equation using simultaneous analysis of remote sensing hyperspectral image and “ideal” hyperspectral image which is free from atmospheric distortions. Experimental results show that proposed method is applicable to perform atmospheric correction.

Regional geographic information systems for gas network monitoring

This work considers the construction principles, architecture, and software of regional geographic information systems designed for monitoring gas networks.

A model-based gradient field descriptor as an efficient tool for recognizing and analyzing digital images

A model-based gradient-field descriptor is proposed to describe digital images. The characteristics of the descriptor, which are regarded as features of a digital image, allow one to efficiently solve the problems of analysis, recognition, and searching of images. Examples of solving such problems using the proposed descriptor are presented.

How to use geoinformation technologies and space monitoring for controlling the agricultural sector in Samara region

A geoinformation system for the agriculture sector of Samara region for the statistical accounting and analysis of agricultural lands is examined in this paper. The architecture of a geoinformation system and its main functions are described. Space monitoring for a sown area is a problem solved by the system. A detailed description of satellite monitoring stages is given in the paper. A method to classify agricultural fields according to crop types by using a time series for the vegetation index NDVI is examined, and its application for observing the results of spring and autumn sowing are presented for the Samara region.

Traffic Flow Forecasting Algorithm Based on Combination of Adaptive Elementary Predictors

In this paper the problem of traffic flow prediction in the transport network of a large city is considered. For fast calculation of predictions, partition of a transport graph into a certain number of subgraphs based on the territorial principle is proposed. Next, we use a dimension reduction method based on principal components analysis to describe the spatio-temporal distribution of traffic flow condition in subgraphs. A short-term (up to 1 h) traffic flow prediction in each subgraph is calculated by an adaptive linear combination of elementary predictions. In this paper, the elementary predictions are Box-Jenkins time-series models, support vector regression, and the method of potential functions. The proposed traffic prediction algorithm is implemented and tested against the actual travel times over a large road network in Samara, Russia.

Computer program for automatic estimation of digital image quality

The software system for identification of optoelectronic digital imaging systems and estimation of their quality is presented. The developed system allows to completely automate the estimation of noise, frequency response of the distoring system, and image resolution.