Computer Optics

184-188

Authors: V.V. Kotlyar, A.A. Kovalev, A.A. Savelyeva

We study a superposition of several identical parallel single-ringed Laguerre-Gaussian beams. Both for the initial plane and for the far field, we show analytically and confirm numerically that if the weighting coefficients of the superposition are real-valued (i.e. all the beams have the same phase, but may have different amplitudes), the net topological charge of the superposition is equal to the topological charge of each constituent beam. It is also shown that a phase delay between the beams allows changing the net topological charge of the superposition. By measuring the net topological charge it is possible to analyze the received optical signal in a data transmission system that utilizes vortex beams, combining spatial multiplexing and orbital angular momentum multiplexing.

196-203

Authors: A.A. Kovalev

In optical communications, it is desirable to know some quantities describing a light field, that are conserved on propagation or resistant to some distortions. Typically, optical vortex beams are characterized by their orbital angular momentum (OAM) and/or topological charge (TC). Here, we study what happens with the OAM of a superposition of two or several optical vortices (with different TCs) when it is distorted by a hard-edge sector aperture. We discover several cases when such perturbation does not violate the OAM of the whole superposition. The first case is when the incident beam consists of two vortices of the same power. The second case is when the aperture half-angle equals an integer number of π divided by the difference between the topological charges. For more than two incident beams, this angle equals an integer number of π divided by the greatest common divisor of all possible differences between the topological charges. For two incident vortex beams with real-valued radial envelopes of the complex amplitudes, the OAM is also conserved when there is a ±(pi)/2 phase delay between the beams. When two beams with the same power pass through a binary radial grating, their total OAM is also conserved.

214-218

Authors: E.M. Pritotskii, A.P. Pritotskaya, M.A. Pankov

Optical characteristics of germanium telluride (GeTe) thin films in multilayer structures are calculated. A model of a multilayer optical coating with a four-level transmission coefficient is developed. Based on the calculated data, thickness values are determined at which the transmittance change is greatest for 1550-nm modulated optical radiation. Experimental samples coated with antireflection GeTe films are synthesized and their optical transmission characteristics are investigated. Combinations of parameters of the multilayer structures for the implementation of contrast transmittance levels are determined. The results of the study represent the implementation of a multilevel artificial optical synapse for neuromorphic processors.

224-231

Authors: V.A. Andreev, A.V. Bourdine, V.A. Burdin, M.V. Dashkov

The paper proposes a method for non-destructive testing of the strength of an optical fiber based on estimates of the energy of nonlinear acoustic emission, based on the use of the tested optical fiber as an acoustic sensor. Models of the processes on which the method is based are presented. Results of experimental studies are presented. Data obtained from testing samples of the optical cables and optical fibers by the proposed method are compared with measurement results for the same samples obtained by the known 2-point method. The error in the estimates of the relative strength of the tested samples of optical fibers is found not to exceed 3.0 %. The results obtained allow us to suggest that the models used are correct and the proposed method shows promise for non-destructive testing of the strength of optical fibers, with the further development of the considered approach showing prospects for the application for non-destructive testing of the strength of optical fibers in a cable, including in-service cables.

239-245

Authors: A.M. Ageev, V.G. Bondarev, V.V. Protsenko

An expedient spectral range of radiation sources for use in the automatic landing system of unmanned aerial vehicles has been substantiated. A method is proposed for synchronizing the photoexposure of the computer vision system and radiation from landmarks for the unambiguous determination of their mutual position. Results of the experimental studies are presented.

252-270

Authors: K.B. Bulatov, E.V. Emelianova, D.V. Tropin, N.S. Skoryukina, Y.S. Chernyshova, A.V. Sheshkus, S.A. Usilin, Z. Ming, J.-C. Burie, M.M. Luqman, V.V. Arlazarov

Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture. Significant amount of research has been published on this topic in recent years, however a chief difficulty for such research is scarcity of datasets, due to the subject matter being protected by security requirements. A few datasets of identity documents which are available lack diversity of document types, capturing conditions, or variability of document field values. In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces, with rich annotation. The dataset contains 72409 annotated images in total, making it the largest publicly available identity document dataset to the date of publication. We describe the structure of the dataset, its content and annotations, and present baseline experimental results to serve as a basis for future research. For the task of document location and identification content-independent, feature-based, and semantic segmentation-based methods were evaluated. For the task of document text field recognition, the Tesseract system was evaluated on field and character levels with grouping by field alphabets and document types. For the task of face detection, the performance of Multi Task Cascaded Convolutional Neural Networks-based method was evaluated separately for different types of image input modes. The baseline evaluations show that the existing methods of identity document analysis have a lot of room for improvement given modern challenges. We believe that the proposed dataset will prove invaluable for advancement of the field of document analysis and recognition.

280-297

Authors: P. Musa, I. Purwanto, D.A. Christie, E.P. Wibowo, R. Irawan

Topography is the study of an area on the earth's surface. This term relates to the land's slope or contour, which is the interval of elevation differences between two adjacent and parallel contour lines. Topography generally presents a three-dimensional model of object surface relief and an identification of land or hilly areas based on horizontal coordinates such as latitude and longitude, and vertical position, namely elevation. The topography is essential information that must be provided in the execution of building or road construction based on the ground contour. The problem which is the ground contour which can provide visualization topography as a three-dimensional (3D) condition of the ground contour is not normal (non-linear). Another problem is that the traditional measurement techniques with wheel rotation only measure distances and cannot represent the trajectory of the ground contour in 3D. The proposed in-depth evaluation of orientation estimation results in the topography accuracy level. This methodology consists of several processes; Inertia and orientation of an object, Distance measurement, Terrestrial topocentric – Euclidean transformation, and Topography visualization. This research designed a prototype and proposed a new visualization method of the ground contours to reconstruct a topography map between three algorithms; Direct Cosine Matrix-3D Coordinate, Madgwick-3D Coordinate, and Complementary Filter. The methodology was tested and evaluated intensively by direct observation at three measurement locations with different difficulty levels. As a result, the Direct Cosine Matrix-3D Coordinate is able to visualize the ground contours by reconstructing a topography map much better than other methods.

308-316

Authors: Yu.Kh. Ganeeva, E.V. Myasnikov

The problem of personal identification plays an important role in information security. In recent years, biometric methods of personal identification have become most relevant and promising. The article presents a study of a method for identifying a person from iris images using a neural network approach at the stages of segmentation and a feature representation from the data. A description of a dataset used to implement the segmentation stage using convolutional neural networks is presented and access to the segmentation masks of the entire dataset is provided. A method is proposed for extracting a feature representation of the data using pretrained convolutional neural networks to solve a problem of iris classification. A comparative analysis of methods for extracting iris features, including classical approaches and a neural network approach, has been carried out. A comparative analysis of classification methods is carried out, including classical machine learning algorithms, namely, support vector machines, random forest, and a k-nearest neighbors method. The results of experimental studies have shown the high quality of the classification based on the proposed approach.

326-334

Authors: J. Pamplona, C. Madrigal, J. Herrera-Ramirez

The three-dimensional perception applications have been growing since Light Detection and Ranging devices have become more affordable. On those applications, the navigation and collision avoidance systems stand out for their importance in autonomous vehicles, which are drawing an appreciable amount of attention these days. The on-road object classification task on three-dimensional information is a solid base for an autonomous vehicle perception system, where the analysis of the captured information has some factors that make this task challenging. On these applications, objects are represented only on one side, its shapes are highly variable and occlusions are commonly presented. But the highest challenge comes with the low resolution, which leads to a significant performance dropping on classification methods. While most of the classification architectures tend to get bigger to obtain deeper features, we explore the opposite side contributing to the implementation of low-cost mobile platforms that could use low-resolution detection and ranging devices. In this paper, we propose an approach for on-road objects classification on extremely low-resolution conditions. It uses directly three-dimensional point clouds as sequences on a transformer-convolutional architecture that could be useful on embedded devices. Our proposal shows an accuracy that reaches the 89.74 % tested on objects represented with only 16 points extracted from the Waymo, Lyft's level 5 and Kitti datasets. It reaches a real time implementation (22 Hz) in a single core processor of 2.3 Ghz.