This article is devoted to the problems of radar sensing. Herein, we have considered the tasks of modeling and recognizing radar images. The modeling technology was based on the independent creation of terrain models and objects, which were then integrated into a three-dimensional (3D) scene. This approach enabled the operative creation of a number of image variants of different classes. Recognition methods and algorithms were based on the use of the so-called conjugacy index as a measure of proximity. At the same time, support subspaces of the minimum dimension were formed by vectors, components of which were samples of the radar image. Problems of higher accuracy of recognition due to a division of classes into subclasses and a combination of the support subspace method with the neural convolutional networks were considered.
The article aimed to provide a sort of new education process including virtual reality based application. At present, in accordance with the established ways of archaeological research, archaeologists are forced to transfer the found samples for long-term storage. In such notation, there is a challenging issue to create a virtual museum with deepening experience user interaction. The modern approaches of the virtual reality were implemented by applying technologies such as the Unreal Engine (UE) and Leap Motion (LM). In the paper, we give the scheme of the implemented development workflow. The ability of interaction with objects using the interface and hand gestures on LM on UE was given.
In this study, we developed a two-stage technology for improving the sharpness of images. In the first stage, the correction was performed using a linear square exponential (SE) filter with a centrally symmetric frequency response in the form of quadratic and exponential functions. This stage included setting the parameters of the SE filter and the actual processing. In the second stage, non-linear correction was carried out. The idea of the filter was to increase the impact of the central value, if it was at the edge of different intensity levels. We assumed that an increase in the absolute value of the weighted average of the differences in the point neighbourhood could be an indicator of such edges. The central point of the reference area belonged to the edge if its value was considerably greater or lesser than the significant number of values in this area. The first experiment confirmed the possibility for the improvement of the quantitative criteria of image restoration by non-linear correction. The second experiment illustrated the increase in the image sharpness obtained using a diffraction Fresnel lens. The proposed technology has opened up prospects for the use of cameras based on diffraction optic elements in mobile devices.
A hyperspectrometer based on the Offner scheme was investigated. Spectral characteristics were studied and calibrated using a standard spectrometer. As a result of estimating the deviations of the spectra of the imaging hyperspectrometer and the reference spectrometer, calibration coefficients were obtained. The reflectance spectra of beets, onions and potatoes under natural solar illumination were experimentally obtained. Based on the analysis of hyperspectral imaging data, an analysis of the distribution of vegetative indices and, in particular, moisture content, was carried out. Analysis of histograms of moisture content index distribution was carried out.
This paper addresses the problem of 3D scene reconstruction in cases when the extrinsic parameters (rotation and translation) of the camera are unknown. This problem is both important and urgent because the accuracy of the camera parameters significantly influences the resulting 3D model. A common approach is to determine the fundamental matrix from corresponding points on two views of a scene and then to use singular value decomposition for camera projection matrix estimation. However, this common approach is very sensitive to fundamental matrix errors. In this paper we propose a novel approach in which camera parameters are determined directly from the equations of the projective transformation by using corresponding points on the views. The proposed decomposition allows us to use an iterative procedure for determining the parameters of the camera. This procedure is implemented in two steps: the translation determination and the rotation determination. The experimental results of the camera parameters estimation and 3D scene reconstruction demonstrate the reliability of the proposed approach.
In this study, the object recognition problem was solved using support plane method. The modelled SAR images were used as features vectors in the recognition algorithm. Radar signal backscattering of objects in different observing poses is presented in SAR images. For real time simulation, we used simple mixture model of Lambertian–specular reflectivity. To this end, an algorithm of ray tracing is extended for simulating SAR images of 3D man-made models. The suggested algorithm of support plane is very effective in objects recognition using SAR images and RCS diagrams.
In the paper we have proposed recognition of object by RCS diagrams method. For modeling the scattering field of 3D objects on underlying surface we had use widely known FDTD method. We have used for distance function in developing method conjugation indices with so-called support plane, is formed within feature vectors of recognition class. We have given the results of recognition experiments with three different methods: support vector method, correlation method with the average class vector and a new support plane method.
The information technology of remotely sensed image analysis is based on system integration of two main concepts:
diffractive optical elements (DOEs) aided multispectral preprocessing and multiscale analysis. Proposed technology
allows to: decrease the threshold of hidden signal detection; detect the signals with unknown form; improve the
performance of subpixel signal detection and estimation in case of limited resolution of sensors. Data preprocessing
method for anomaly detection on the base of DOE technology is developed. DOE-based technology is implemented and
tested. Nonparametric statistic methods for signal detection and estimation are proposed. Multiresolution image analysis
methods are applied for anomaly detection and estimation.
We consider topics related to the construction of reconstructing filters for the correction of nonisoplanar distortions of defocusing type. The problem is tackled by the direct identification of shift-invariant models and reconstructing filters on small image fragments. We propose and study a new method for the selection of fragments based on analyzing the conjugation of vectors of independent variables with zero-space.
The paper deals with the development of identification algorithms for adaptive control systems, as well as with specific features related to the realization of the algorithms. We develop an algebraic approach to the construction of identification algorithms. This involves a more realistic formulation of the task as compared with that adopted in the theory of statistical estimation. In particular, we construct the procedures of analysis and selection of the most informative data, based on the degrees of conditionality proposed in the paper. A qualitative theory of identification is developed.
KEYWORDS: Image processing, Error analysis, Data modeling, Chemical elements, 3D image processing, Channel projecting optics, Image enhancement, Tolerancing, Systems modeling, Solar thermal energy
In the present paper we tackle the problem of the identification of optical distorting systems. The identification is realized on the informative fragments, which are determined using the conditionality estimates of the matrixes. We construct simple formal procedures for evaluating the degree of conditionality with the objective to determine image informative fragments that permit the identification to be performed with desired accuracy.
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