Vladislav Pinchukov

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.

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.

Close-range photogrammetry is widely used to measure surface shapes and diagnose deformation. Usually, a stereo system of video cameras is used to register images of the measured object from several different angles. The surface shape is determined by triangulating a set of 2D points from these images. Triangulation uses the stereo system calibration parameters, which are determined before the experiment. Measurements during conditions with increased vibration loads can lead to a change in the relative position of the cameras of the stereo system (decalibration). This leads to a change in the actual calibration parameters and an increase in the measurement error. The decalibration problem can be solved using multidimensional optimization algorithms. To verify their calculation's results it is proposed to use a computer and physical modeling of decalibration of a video camera stereo system in laboratory conditions. The paper presents the implementation of the optimizing algorithm for the external parameters of a stereo system and the results of its performance during the experimental investigations.