Crosscorrelation image processing for surface shape reconstruction using fiducial markers

Shmatko, E. V.; Pinchukov, V.V.; Bogachev, A D; Poroykov, A. Yu.

doi:10.1088/1742-6596/2127/1/012030

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…The maximum of the correlation function makes it possible to determine the displacement of the interrogation windows relative to each other and, on this basis, to determine the corresponding points for which a triangulation is calculated to estimate the three-dimensional coordinates of the surface. In addition, ArUco fiducial markers [ 56 ], which are also applied to the measured surface, are used for initial image matching [ 57 ].…”

Section: Resultsmentioning

confidence: 99%

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

Poroykov,

Pechinskaya,

Shmatko

et al. 2023

Sensors

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Close-range photogrammetry methods are widely used for non-contact and accurate measurements of surface shapes. These methods are based on calculating the three-dimensional coordinates of an object from two-dimensional images using special digital processing algorithms. Due to the relatively complex measurement principle, the accurate estimation of the photogrammetric measurement error is a non-trivial task. Typically, theoretical estimations or computer modelling are used to solve this problem. However, these approaches cannot provide an accurate estimate because it is impossible to consider all factors that influence the measurement results. To solve this problem, we propose the use of physical modelling. The measurement results from the photogrammetric system under test were compared with the results of a more accurate reference measurement method. This comparison allowed the error to be estimated under controlled conditions. The test object was a flexible surface whose shape could vary smoothly over a wide range. The estimation of the measurement accuracy for a large number of different surface shapes allows us to obtain new results that are difficult to obtain using standard approaches. To implement the proposed approach, a laboratory system for the error estimation of close-range photogrammetric measurements was developed. The paper contains a detailed description of the developed system and the proposed technique for a comparison of the measurement results. The error in the reference method, which was chosen to be phasogrammetry, was evaluated experimentally. Experimental testing of the stereo photogrammetric system was performed according to the proposed technique. The obtained results show that the proposed technique can reveal dependencies that may not be detected by standard approaches.

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Section: Resultsmentioning

confidence: 99%

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

Poroykov,

Pechinskaya,

Shmatko

et al. 2023

Sensors

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“…The result of modeling in form of the mean square error of the true flow (defined in the modeling) with the measured flow is shown in Figure 1. Processing by cross-correlation was performed using a software package of our own design [23]. Processing parameters: interrogation window size 24×24 pixels, interrogation window offset 4 pixels, approximation of correlation peak by Gaussian distribution.…”

Section: Computer Modelingmentioning

confidence: 99%

Computer and Physical Modeling for the Estimation of the Possibility of Application of Convolutional Neural Networks in Close-Range Photogrammetry

Pinchukov

Poroykov

Shmatko

et al. 2023

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Close-range photogrammetry is widely used to measure the surface shape of various objects and its deformations. The classic approach for this is to use a stereo pair of images, which are captured from different angles using two digital video cameras. The surface shape is measured by triangulating a set of corresponding two-dimensional points from these images using a predetermined location of cameras relative to each other. Various algorithms are used to find these points. Several photogrammetry methods use cross-correlation for this purpose. This paper discusses the possibility of replacing the correlation algorithm with neural networks to determine displacements of small areas in the images. They allow increasing the calculation speed and the spatial resolution of the measurement results. To verify the possibility of using convolutional networks for photogrammetry tasks, computer and physical modeling were carried out. For the first test, a set of synthetically generated images representing images of the Particle Image Velocimetry method was used. The displacements of particles in the images are known, it allows to estimate the accuracy of processing of such images. For the second test, a series of experimental images with surfaces with different deformation was obtained. Computational experiments were performed to process synthetic and experimental images using selected neural networks and a classical cross-correlation algorithm. The limitations on the use of the compared algorithms were determined and their error in reconstructing the three-dimensional shape of the surface was evaluated. Computer and physical modeling have shown the operability and efficiency of neural networks for processing photogrammetry images.

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“…Один из методов для восстановления трехмерной поверхности основан на кросскорреляционной обработке стереопар изображений. Такой метод называется методом корреляции фоновых изображений (МКФИ, в англоязычных источниках IPCT) [1][2][3][4]. Он основан на алгоритмах обработки другого метода -анемометрии по изображению частиц (PIV) и является другим вариантом метода корреляции цифровых изображений (DIC).…”

Section: Introductionunclassified

“…Для восстановления формы поверхности был использован метод IPCT по алгоритму, описанному в [4]. Первый этап алгоритма -это поиск кодовых маркеров на изображении для проведения перспективного преобразования изображений стереопары.…”

Section: Introductionunclassified

Exploring the Application of Convolutional Neural Networks for Photogrammetric Image Processing

Pinchukov

Poroykov

Shmatko

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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Close-range photogrammetry is widely used to measure the surface shape of various objects and its deformations. Usually, a stereo pair of images of the object under study, obtained from different angles by means of several digital video cameras, is used for this purpose. The surface shape is measured by triangulating a set of corresponding two-dimensional points from these images using a predetermined location of cameras relative to each other. Various algorithms are used to find these points. Several photogrammetric methods use cross-correlation for this purpose. This paper discusses the possibility of replacing the correlation algorithm with neural networks to determine offsets in the images. They allow to increase the calculation speed and the spatial resolution of the measurement results. To verify the possibility of their application, a series of experimental images of surfaces with different deformations were obtained. Computational experiments were performed to process these images using selected neural networks and a classical cross-correlation algorithm. The limitations on the use of the compared algorithms were determined and their error in restoring the three-dimensional shape of the surface was estimated. The physical simulation to verify the selected neural networks for image processing for the task of photogrammetry showed their performance and efficiency.

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Crosscorrelation image processing for surface shape reconstruction using fiducial markers

Cited by 5 publications

References 6 publications

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

Computer and Physical Modeling for the Estimation of the Possibility of Application of Convolutional Neural Networks in Close-Range Photogrammetry

Exploring the Application of Convolutional Neural Networks for Photogrammetric Image Processing

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