Ms. Marina M. Zhdanova Profile

Marina M. Zhdanova

SPIE Involvement:

Author

Publications (53)

Proceedings Article | 28 November 2023 Poster + Paper

Poster + Paper

Real‐time deep learning semantic segmentation for 3-D augmented reality

V. Voronin, E. Semenishchev, A. Zelensky, M. Zhdanova, N. Gapon

Proceedings Volume 12772, 127720L (2023) https://doi.org/10.1117/12.2691152

KEYWORDS: Augmented reality, Image segmentation, Semantics, Neural networks, Video, RGB color model, Object detection, Detection and tracking algorithms, Deep learning

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Augmented reality is a visualization technology that displays information by adding virtual images to the real world. In many cases, augmented reality requires recognition of the current scene. Extracting foreground objects from a video in real-time on limited hardware, such as a smartphone, is demanding. The augmented reality for the environment must have a model in which it is clear where it is necessary to detect the object or apply a mask. One way to recognize a scene in the current context without prior information is to use semantic segmentation techniques. This article proposes a new neural network architecture for efficient semantic image segmentation in the task of building augmented reality. The developed architecture is based on the combination of Shufflenet V2 and DPC, which provides good performance due to the balance between predictive accuracy and efficiency. First, the ShuffleNet V2 neural network architecture obtains features from RGB images. The resulting feature maps are then passed to one of the Deeplab V3+ Dense Prediction Cell encoders. At the final stage, the features are decoded by bilinear interpolation to create segmentation masks. The augmented reality construction algorithm is based on the ARCore framework and the OpenGL interface for embedded systems. The proposed approach for recognizing scene objects in augmented reality uses semantic segmentation, providing real-time information. The implementation of the algorithm shows that the detected objects can be tracked in 3-D space using visual-inertial odometry without resorting to constantly updating the environment model. The frequency of object detection and semantic mask generation can be reduced, resulting in battery and processing power savings, which is critical for mobile and embedded systems. The semantic information provided by these solutions can be used in autonomous driving, robotics navigation, localization, and scene recognition in conditions of limited resources. In augmented reality, the proposed approach can remove objects from a scene, draw attention to objects, or provide scene recognition to software logic. The experiment results confirmed the high efficiency of the proposed method compared to the state-of-the-art techniques for real‐time 3-D augmented reality construction.

Proceedings Article | 27 November 2023 Poster + Paper

Poster + Paper

Image inpainting with deep adaptive generative models for multimedia computing

V. Voronin, N. Gapon, M. Zhdanova, O. Tokareva, E. Semenishchev

Proceedings Volume 12767, 127671V (2023) https://doi.org/10.1117/12.2691343

KEYWORDS: Multimedia, Neural networks, Image processing, Deep learning

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Proceedings Article | 27 November 2023 Poster + Paper

Poster + Paper

Visible watermarks inpainting using deep learning for multimedia application

N. Gapon, E. Yadrets, V. Voronin, A. Sosnovsky, M. Zhdanova, E. Semenishchev

Proceedings Volume 12767, 127671U (2023) https://doi.org/10.1117/12.2690884

KEYWORDS: Digital watermarking, Multimedia, Deep learning, Visualization, Photography, Internet, Intellectual property, Industrial applications, Distortion

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Proceedings Article | 27 November 2023 Poster + Paper

Poster + Paper

A multimodal visual guided robot collaborative system based on the classification of multi-class human motion

V. Voronin, M. Zhdanova, O. Tokareva, E. Semenishchev, A. Zelensky, N. Gapon

Proceedings Volume 12766, 1276614 (2023) https://doi.org/10.1117/12.2691154

KEYWORDS: Visualization, Robotic systems, Classification systems, Binary data, Optical sensors, Neural networks, Modeling, Fourier transforms, Detection and tracking algorithms, Convolutional neural networks

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Proceedings Article | 27 November 2023 Poster + Paper

Poster + Paper

3D dense micro-block difference descriptor for gastrointestinal disease detection

M. Zhdanova, V. Voronin, O. Tokareva, E. Semenishchev, A. Zelensky, N. Gapon

Proceedings Volume 12770, 127703S (2023) https://doi.org/10.1117/12.2691162

KEYWORDS: Video, Diseases and disorders, Endoscopy, Polyps, Machine learning, Cancer detection, Visualization, Neural networks, Image quality

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Proceedings Article | 27 November 2023 Poster + Paper

Poster + Paper

Attention map-guided multi-scale haze removal method for industrial inspection system

V. Voronin, N. Gapon, M. Zhdanova, E. Semenishchev, A. Zelensky

Proceedings Volume 12769, 127691R (2023) https://doi.org/10.1117/12.2690890

KEYWORDS: Inspection, Air contamination, Image processing, 3D image enhancement, Image fusion, Atmospheric particles, Image enhancement, Gamma correction, Detection and tracking algorithms, 3D image processing

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Proceedings Article | 27 November 2023 Poster + Paper

Poster + Paper

Deep learning-based depth map defect removal for industrial applications

V. Voronin, N. Gapon, M. Zhdanova, O. Tokareva, I. Khamidullin, E. Semenishchev

Proceedings Volume 12769, 127691Q (2023) https://doi.org/10.1117/12.2690886

KEYWORDS: Depth maps, Image restoration, Industrial applications, Manufacturing, Defect detection, Contour modeling, Autoregressive models, Signal attenuation, Robotics, Reconstruction algorithms

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Proceedings Article | 15 August 2023 Poster + Paper

Poster + Paper

Block-based multi-scale image enhancement method for industrial inspection system

V. Voronin, N. Gapon, M. Zhdanova, E. Semenishchev, A. Zelensky, Yu. Ilyukhin

Proceedings Volume 12618, 1261834 (2023) https://doi.org/10.1117/12.2678111

KEYWORDS: Image enhancement, Image processing, Image fusion, 3D image enhancement, Inspection, Image quality, Gamma correction, Air contamination

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Proceedings Article | 9 August 2023 Poster + Paper

Poster + Paper

Action recognition algorithm from multimodal images for human-robot collaboration systems

V. Voronin, M. Zhdanova, E. Semenishchev, A. Zelensky, N. Gapon, Yu. Ilyukhin

Proceedings Volume 12621, 126211A (2023) https://doi.org/10.1117/12.2678112

KEYWORDS: Detection and tracking algorithms, Action recognition, Fourier transforms, Image processing, Image fusion, Image filtering, 3D image processing, Video

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Proceedings Article | 7 August 2023 Poster + Paper

Poster + Paper

Deep learning-based image defect detection and removal in manufacturing

N. Gapon, V. Voronin, M. Zhdanova, R. Sizyakin, E. Semenishchev, Yu Ilyukhin

Proceedings Volume 12624, 126241Q (2023) https://doi.org/10.1117/12.2682045

KEYWORDS: Defect detection, Image restoration, Image processing, Image enhancement, Image filtering, Neural networks, Reconstruction algorithms, CAD systems, Manufacturing

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Proceedings Article | 16 June 2023 Poster + Paper

Poster + Paper

Defect detection and removal for depth map quality enhancement in manufacturing with deep learning

N. Gapon, V. Voronin, R. Sizyakin, M. Zhdanova, E. Semenishchev, A. Zelensky

Proceedings Volume 12524, 125240L (2023) https://doi.org/10.1117/12.2666560

KEYWORDS: Depth maps, Defect detection, Image fusion, Image restoration, Manufacturing, Image filtering, Reconstruction algorithms, Feature fusion, Neural networks

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Proceedings Article | 15 June 2023 Poster + Paper

Poster + Paper

Action recognition from thermal videos using skeleton information and 3D local binary dense micro-block difference

V. Voronin, M. Zhdanova, N. Gapon, E. Semenishchev, A. Zelensky

Proceedings Volume 12526, 125260W (2023) https://doi.org/10.1117/12.2666557

KEYWORDS: 3D image processing, Video, Action recognition, Tunable filters, RGB color model, Image processing, Image fusion

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Proceedings Article | 13 June 2023 Poster + Paper

Poster + Paper

Real-time robotic hand control using human gesture recognition

V. Egipko, V. Voronin, N. Gapon, M. Zhdanova, E. Semenishchev, A. Zelensky

Proceedings Volume 12528, 125280E (2023) https://doi.org/10.1117/12.2666559

KEYWORDS: Gesture recognition, Robots, Control systems, Robotics, Network architectures, Image processing, Robotic systems

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Proceedings Article | 13 June 2023 Poster + Paper

Poster + Paper

Formation of an algorithm for determining the degree of human involvement based on the analysis of the movement of the pupils of the operator and data on the position of the head and body

Evgenii Semenishchev, Marina Zhdanova, Aleksandr Zelensky, Inessa Gracheva, Daniil Lyakhov, Nikita Mitugov, Viacheslav Voronin

Proceedings Volume 12528, 125280F (2023) https://doi.org/10.1117/12.2666763

KEYWORDS: Image processing, Education and training, Machine vision, Evolutionary algorithms

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Proceedings Article | 4 January 2023 Poster + Paper

Poster + Paper

Pre-processing and data fusion of a complex image obtained by a pair of sensors with a free arrangement

Evgenii Semenishchev, Viacheslav Voronin, Aleksandr Zelensky, Marina Zhdanova, Roman Sizyakin, Solavat Urunov

Proceedings Volume 12317, 123171D (2023) https://doi.org/10.1117/12.2646167

KEYWORDS: Image fusion, Sensors, Image processing, Image sensors, Machine vision, Control systems, Robotics

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Proceedings Article | 19 December 2022 Poster + Paper

Poster + Paper

Preprocessing to improve the accuracy of recognition of radioelement components in automated assembly devices

Evengii Semenishchev, Aleksandr Zelensky, Marina Zdanova, Yuriy Ilukhin, Viacheslav Voronin, Roman Sizyakin

Proceedings Volume 12319, 123191Z (2022) https://doi.org/10.1117/12.2646164

KEYWORDS: Machine vision, Sensors, Image processing, Electronics, Robotics

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Proceedings Article | 7 December 2022 Paper

Paper

Algorithm for increasing the discriminability of sections of vortex structures and wind flows recorded by radio frequency stations using Doppler effect analysis

Evgenii Semenishchev, Aleksandr Zelensky, Marina Zhdanova, Nikolay Gapon, Aleksandr Gavlicky, Viacheslav Voronin

Proceedings Volume 12274, 122741B (2022) https://doi.org/10.1117/12.2641156

KEYWORDS: Doppler effect, Image processing, Algorithm development, Signal processing

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Proceedings Article | 2 November 2022 Paper

Paper

Deep visible and thermal image fusion for enhancement visibility for surveillance application

V. Voronin, M. Zhdanova, N. Gapon, A. Alepko, A. Zelensky, E. Semenishchev

Proceedings Volume 12271, 122710P (2022) https://doi.org/10.1117/12.2641857

KEYWORDS: Image fusion, Image processing, Infrared imaging, Image enhancement, Thermal modeling, Surveillance

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Proceedings Article | 2 November 2022 Paper

Paper

Development of a fusion technique and an algorithm for merging images recorded in the IR and visible spectrum in dust and fog

Evgenii Semenishchev, Aleksandr Zelensky, Andrey Alepko, Marina Zhdanova, Viacheslav Voronin, Yury Ilyukhin

Proceedings Volume 12271, 122710O (2022) https://doi.org/10.1117/12.2641155

KEYWORDS: Image fusion, Infrared imaging, Image processing, Data processing, Cameras, Algorithm development, Sensors

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Proceedings Article | 28 October 2022 Poster + Paper

Poster + Paper

Block-based multi-scale haze image enhancement method for surveillance application

V. Voronin, M. Zhdanova, I. Khamidullin, O. Tokareva, A. Zelensky, E. Semenishchev

Proceedings Volume 12275, 122750K (2022) https://doi.org/10.1117/12.2641136

KEYWORDS: Image enhancement, Image fusion, Image processing, Visualization, Surveillance, Image visualization, Image contrast enhancement

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Proceedings Article | 28 October 2022 Poster + Paper

Poster + Paper

Formation of an algorithm for increasing the accuracy of restoring the boundaries of objects obtained to create 3D structures

Evgenii Semenishchev, Aleksandr Zelensky, Marina Zhdanova, Yury Ilyukhin, Roman Sizyakin, Viacheslav Voronin

Proceedings Volume 12276, 122760L (2022) https://doi.org/10.1117/12.2641158

KEYWORDS: Cameras, Machine vision, Image segmentation, 3D image processing, 3D modeling, Smoothing, Algorithm development

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Proceedings Article | 28 October 2022 Poster + Paper

Poster + Paper

Video segmentation on static and dynamic textures using a quaternion framework

A. Zelensky, V. Voronin, M. Zhdanova, A. Siryakov, V. Egipko, S. Urunov, E. Semenishchev

Proceedings Volume 12276, 122760M (2022) https://doi.org/10.1117/12.2641697

KEYWORDS: Image segmentation, Video, Video surveillance, Neural networks, Convolution, RGB color model, Machine vision

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Proceedings Article | 28 October 2022 Presentation + Paper

Presentation + Paper

Action recognition algorithm from visual sensor data for contactless robot control systems

V. Voronin, M. Zhdanova, I. Khamidullin, E. Semenishchev, A. Zelensky, Yu. Ilyukhin

Proceedings Volume 12276, 1227604 (2022) https://doi.org/10.1117/12.2642003

KEYWORDS: Neural networks, Sensors, Control systems, Video, Cameras, Algorithm development, Software development, Visualization, Robotics

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Proceedings Article | 26 October 2022 Presentation + Paper

Presentation + Paper

Improving the quality and visibility of objects in images captured in difficult weather conditions

Evgenii Semenishchev, Aleksandr Zelensky, Yury Ilyukhin, Nikolay Gapon, Marina Zhdanova, Viacheslav Voronin

Proceedings Volume 12266, 122660I (2022) https://doi.org/10.1117/12.2641157

KEYWORDS: Visibility through fog, Visibility, Image fusion, Image processing, Air contamination, Control systems

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Proceedings Article | 8 June 2022 Paper

Paper

Algorithm for the formation of closed contours of objects represented by small piecewise-discontinuous functions for the tasks of constructing stable features and automated selection of areas of complex shape when processing scenes obtained in the infrared and optical ranges

Evgenii Semenishchev, Sos Agaian, Nikolay Mitugov, Marina Zdanova, Aleksandr Zelensky, Viacheslav Voronin

Proceedings Volume 12122, 121220W (2022) https://doi.org/10.1117/12.2623049

KEYWORDS: Infrared radiation, Infrared imaging, Image analysis, Edge detection, Image segmentation, Image processing

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Proceedings Article | 31 May 2022 Paper

Paper

Algorithm for creating a single image of the cutting machine processing field and automated object detection when cutting parts

Evgenii Semenishchev, Sos Agaian, Aleksandr Zelensky, Dmitry Orlov, Marina Zdanova, Viacheslav Voronin

Proceedings Volume 12124, 121240M (2022) https://doi.org/10.1117/12.2623046

KEYWORDS: Image processing, Image enhancement, Robotics, Image resolution, Detection and tracking algorithms, Image analysis, Cutting equipment, Laser cutting

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Proceedings Article | 27 May 2022 Poster + Paper

Poster + Paper

3D shape object reconstruction with non-Lambertian surface from multiple views based on deep learning

V. Voronin, V. Frantc, E. Semenishchev, M. Zhdanova, A. Zelensky, S. Agaian

Proceedings Volume 12100, 121000S (2022) https://doi.org/10.1117/12.2623130

KEYWORDS: Neural networks, Optical properties, Light sources and illumination, Structured light, Reflection, Reconstruction algorithms

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The 3-D reconstruction is relative to constructing a mathematical representation of the scene geometric. In most existing approaches, Lambert's law of the object reflectivity in the scene is explicitly or implicitly assumed. In practice, the law of reflectivity of scene objects differs from Lambert's law as, for example, objects with properties: semi-transparent, transparent, specular, with subsurface scattering effects. This paper proposes an algorithm to estimate a surface reflectance model of 3-D shape and parameters from multiple views. The proposed algorithm includes the following steps: 1) determination of the optical properties of the scanned scene by separating the direct and global lighting components using high-frequency templates; 2) generation of a set of patterns of structured light, the structure of which depends on the optical properties of the scanned scene; 3) scanning the scene using the generated structured light patterns for views non- Lambertian surface; 4) construction of a 3-D model of the scene by triangulating methodology. To solve the problem of determining the views non-Lambertian surface, a 3-D reconstruction algorithm based on a convolutional neural network is proposed. To train the neural network, we apply two stages. At the first stage, the encoder is trained for the descriptor description of the input image. In the second step, a fully connected neural network is added to the encoder for regression for choosing the best views. The coder is trained using the generative adversarial methodology to construct a descriptor description that stores spatial information and information about the optical properties of surfaces located in different areas of the image. The codec network is trained to recover the defect map (depends directly on the sensor and scene properties) from a color image. The architecture of the neural network (generator) is based on the U-Net architecture. As a result, this method uses non-Lambertian properties, and it can compensate for triangulation reconstruction errors caused by viewdependent reflections. Experimental results on both synthetic and real objects are given.

Proceedings Article | 27 May 2022 Poster + Paper

Poster + Paper

Quality assessment with deep learning for imaging applications

V. Voronin, A. Zelensky, M. Zhdanova, E. Semenishchev, V. Frantc, A. Siryakov

Proceedings Volume 12100, 121000P (2022) https://doi.org/10.1117/12.2619801

KEYWORDS: Video, Image quality, Visualization, Image processing, Video coding, Neural networks, Video processing

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Proceedings Article | 20 May 2022 Poster + Paper

Poster + Paper

Algorithm for the automated determination of the forms to electrical discharge processes used to analyze the formed electrolyte plasma and predict the properties of the formed metal oxide layers

Aleksandr Zelensky, Sergey Zhukov, Olga Zhukova, Marina Zdanova, Viacheslav Voronin, Evgenii Semenishchev

Proceedings Volume 12136, 1213619 (2022) https://doi.org/10.1117/12.2625837

KEYWORDS: Oxides, Metals, Plasma, Image processing, Feature extraction, Visual analytics, Statistical analysis

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Proceedings Article | 17 May 2022 Paper

Paper

Technique for analyzing the working table on a robotic complex based on the study of point data in a two-dimensional measurement space

Evgenii Semenishchev, Aleksandr Zelensky, Andrey Alepko, Marina Zdanova, Viacheslav Voronin

Proceedings Volume 12138, 121380W (2022) https://doi.org/10.1117/12.2625836

KEYWORDS: Robotics, Analytical research, Space robots, Smoothing, Statistical analysis, Data processing, Data conversion, Signal processing

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The article discusses the analysis technique working table for a robotic complex based on the study of point data in a twodimensional measurement space. The resulting data is a two-dimensional data set. Data processing is performed iteratively, using a smoothing and interpolation method. The method is based on the use of a multicriteria objective function. Minimization of the objective function is performed simultaneously by the standard deviation of the input data from the values obtained as a result of processing and the criterion minimizing the mean square of the difference between the values generated as a result of processing. The adjustment parameter allows you to set the weight of the criterion. The use of such a filter allows you to automatically set the degree of smoothness of the output function and determine the areas that have a deviation relative to the measurement error threshold set by the operator. At the second iteration, the marked areas and the area next to them are measured with the minimum grid spacing. Areas that are not marked as locally damaged are analyzed with a reduced step (mismatched points) and reprocessed by a multi-criteria method. If new errors are found, the area is rescanned, otherwise it is accepted as acceptable for use. The resulting three-dimensional matrix makes it possible to evaluate areas with large errors (deviations) at standard displacements of the working tool and the working table, and to calculate the compensation parameters of the displacements. As field data, we used data from the analysis of deviations obtained by analyzing the desktop with a minimum grid step of measurements and a shift in three coordinates of the machine and the proposed approach. The result obtained made it possible to identify the same areas of deviations as the complete one obtained during the full measurement cycle. For various conditions, it was possible to reduce the analysis time up to 10 times, in some cases it was more than 12 hours. Tables of nature data and examples of calculation of predictive values of final analysis cycles are given.

Proceedings Article | 17 May 2022 Poster + Paper

Poster + Paper

Multi-level deep learning depth and color fusion for action recognition

A. Zelensky, V. Voronin, M. Zhdanova, N. Gapon, O. Tokareva, E. Semenishchev

Proceedings Volume 12138, 121380Y (2022) https://doi.org/10.1117/12.2626000

KEYWORDS: Image fusion, Image processing, Computer programming, Neural networks, Process modeling, Video, Image quality

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Proceedings Article | 20 June 2021 Poster + Paper

Poster + Paper

Automated visual inspection of fabric image using deep learning approach for defect detection

V. Voronin, R. Sizyakin, M. Zhdanova, E. Semenishchev, D. Bezuglov, A. Zelemskii

Proceedings Volume 11787, 117870P (2021) https://doi.org/10.1117/12.2592872

KEYWORDS: Defect detection, Optical inspection, Image enhancement, Neural networks, Manufacturing, Machine learning, Detection and tracking algorithms, Databases, Control systems

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Proceedings Article | 12 April 2021 Presentation + Paper

Presentation + Paper

No-reference transform-based quality assessment for imaging applications

V. Voronin, M. Zhdanova, A. Zelensky, S. Agaian

Proceedings Volume 11734, 1173405 (2021) https://doi.org/10.1117/12.2588000

KEYWORDS: Image enhancement, Image quality, Quality measurement, Image contrast enhancement, Imaging systems, Image processing, Image compression, Image acquisition, Databases

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Proceedings Article | 12 April 2021 Paper

Paper

Determination of tool cutting surface quality from analysis on a group of images and the combined data

Evgenii Semenishchev, Viacheslav Voronin, Aleksandr Zelensky, Andrey Alepko, Marina Zhdanova, Sos Agaian

Proceedings Volume 11734, 117340A (2021) https://doi.org/10.1117/12.2587804

KEYWORDS: Shape analysis, Visible radiation, Light sources and illumination, Infrared imaging, Systems modeling, Sensors, Robotic systems, Materials processing, Materials analysis, Infrared radiation

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Proceedings Article | 12 April 2021 Presentation + Paper

Presentation + Paper

Human action recognition with 3D local binary patterns and dense micro-block difference description

M. Zhdanova, V. Voronin, E. Semenishchev, Yu. Ilyukhin, A. Zelensky

Proceedings Volume 11729, 117290H (2021) https://doi.org/10.1117/12.2588291

KEYWORDS: Binary data, Video surveillance, Video, Detection and tracking algorithms, 3D image processing, Stochastic processes, Robot vision, Medical research, Medical equipment, Machine vision

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Proceedings Article | 12 April 2021 Poster + Paper

Poster + Paper

Algorithm for fusing data obtained by thermal, 3D, and the visible range cameras

E. Semenishchev, V. Voronin, S. Agaian, M. Zhdanova, A. Zelensky

Proceedings Volume 11732, 117320E (2021) https://doi.org/10.1117/12.2587922

KEYWORDS: Cameras, Image resolution, Information visualization, Image processing, Data modeling, 3D modeling, X-rays, X-ray imaging, Visualization, Visible radiation

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Proceedings Article | 14 October 2020 Presentation + Paper

Presentation + Paper

Algorithm for image enhancement and subsequent stitching of combined multichannel images

E. Semenishchev, V. Voronin, S. Agaian, A. Zelensky, U. Ilyukhin, M Zhdanova

Proceedings Volume 11533, 1153307 (2020) https://doi.org/10.1117/12.2574197

KEYWORDS: Image enhancement, Image fusion, Image processing, Control systems, Infrared imaging, Cameras, Image analysis, Video, Algorithm development, Sensors

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Proceedings Article | 14 October 2020 Presentation + Paper

Presentation + Paper

Solving the problem of quickly separating objects into thermal structures in IR images based on analyzing temperature gradients

E. Semenishchev, V. Voronin, S. Agaian, A. Zelensky, M. Zhdanova

Proceedings Volume 11537, 115370O (2020) https://doi.org/10.1117/12.2574190

KEYWORDS: Thermography, Infrared imaging, Image analysis, Mobile devices, Unmanned aerial vehicles, Control systems, Computing systems, Data processing, Data analysis, Robotic systems

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When analyzing images in the far-infrared spectrum, the problem of separation of objects arises on the given temperature ranges. The simple threshold separation is hindered by the presence of a noise component. This task is most relevant when used to on weak computing systems and organizing data processing onboard the device (or mobile device). This problem may arise during data analysis: during processing onboard aircraft (UAV), automated control systems for robotic systems, security and access control systems, etc. In article proposes an approach that allows solving the problem of separating a thermal imaging image to objects within specified ranges. The approach is step-by-step image processing using the filtering and blurring method based on multicriteria image preprocessing. A method for changing the bitrate and a method for detecting objects based on the density of similar pixels in a given analysis sliding window. The filtering and blurring method is adapted and accelerated for implementation on low-performance computing modules. As a result of developing the method, we managed to lower the computational requirements. However, this slightly increases the time of preprocessing. The article describes in detail the algorithm for changing the multicriterial method, shows of changes in characteristics and their influence on the result. A method is proposed for changing the gradation of IR image ranges, based on multiple changes on the section gradient. The on a step-by-step merging algorithm for adjacent ranges is based on the use of adaptive weighting factors. The coefficients are determined automatically and depend on the frame. The method of final separation into objects is based on an analysis in the sliding window, we analysis of the number of different elements and the allocation of close objects in groups. The methods and algorithms proposed in the work are adapted for calculations on devices with reduced requirements for computational costs. On the set of test images obtained by portable thermal imaging cameras (SEEK with a resolution of 320 by 240 pixels) from the UAV, car, and street, we present the effectiveness of the application of the approach introduced in the article.

Proceedings Article | 13 October 2020 Presentation + Paper

Presentation + Paper

Human activity recognition for efficient human-robot collaboration

M. Zhdanova, V. Voronin, E. Semenishchev, Yu. Ilyukhin, A. Zelensky

Proceedings Volume 11543, 115430K (2020) https://doi.org/10.1117/12.2574133

KEYWORDS: Video, Robots, Information visualization, Control systems, Detection and tracking algorithms, Manufacturing, Systems modeling, Visual process modeling, Machine vision, Image processing

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Proceedings Article | 13 October 2020 Poster + Paper

Poster + Paper

Missing area reconstruction in 3D scene from multi-view satellite images for surveillance applications

V. Voronin, N. Gapon, M. Zhdanova, E. Semenishchev, Y. Cen, A. Zelensky

Proceedings Volume 11542, 115420P (2020) https://doi.org/10.1117/12.2574208

KEYWORDS: 3D image processing, Satellite imaging, Earth observing sensors, Satellites, Surveillance, 3D image reconstruction, 3D applications, 3D modeling, Cameras, Image processing

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Proceedings Article | 24 August 2020 Presentation + Paper

Presentation + Paper

Gastric polyps detection based on endoscopic video using modified dense micro-block difference descriptor

M. Zhdanova, V. Voronin, E. Semenishchev, O. Balabaeva, A. Zelensky

Proceedings Volume 11511, 115110D (2020) https://doi.org/10.1117/12.2571398

KEYWORDS: Video, Endoscopy, Fourier transforms, Optical filters, Visualization, 3D image processing, Machine learning

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Proceedings Article | 27 May 2020 Paper

Paper

Automatic search for thermal energy leaks in the form of thin gaps during the audit of buildings based on mobile systems

Evgenii Semenishchev, Viacheslav Voronin, Aleksandr Zelensky, Marina Zhdanova, Irina Tolstova

Proceedings Volume 11399, 113990U (2020) https://doi.org/10.1117/12.2559057

KEYWORDS: Buildings, Thermography, Image fusion, Sensors, Data fusion, Infrared imaging, Mathematical modeling

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Proceedings Article | 22 May 2020 Paper

Paper

Fusion of color and depth information for human actions recognition

V. Voronin, M. Zhdanova, E. Semenishchev, A. Zelensky, O. Tokareva

Proceedings Volume 11423, 114231C (2020) https://doi.org/10.1117/12.2560130

KEYWORDS: Video, Image fusion, RGB color model, Fourier transforms, 3D image processing, Image processing, 3D modeling

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Proceedings Article | 31 January 2020 Paper

Paper

Depth-map inpainting using learned patch-based propagation

Nikolay Gapon, Viacheslav Voronin, Roman Sizyakin, Marina Zhdanova, Alexander Zelensky

Proceedings Volume 11433, 1143338 (2020) https://doi.org/10.1117/12.2559452

KEYWORDS: Cameras, Neural networks, Image processing, Image restoration, Reconstruction algorithms, Robotics, Intelligence systems

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Proceedings Article | 24 September 2019 Paper

Paper

Rain and snow removal using multi-guided filter and anisotropic gradient in the quaternion framework

V. Voronin, E. Semenishchev, M. Zhdanova, R. Sizyakin, A. Zelenskii

Proceedings Volume 11169, 111690S (2019) https://doi.org/10.1117/12.2534744

KEYWORDS: Detection and tracking algorithms, Evolutionary algorithms, RGB color model, Machine vision, Computer vision technology, Object recognition, Visual process modeling, Convolution, Neural networks

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Proceedings Article | 24 June 2019 Presentation + Paper

Presentation + Paper

Medical image segmentation by combing the local, global enhancement, and active contour model

V. Voronin, Evgenii Semenishchev, Marina Pismenskova, O. Balabaeva, S. Agaian

Proceedings Volume 10999, 109990Q (2019) https://doi.org/10.1117/12.2519584

KEYWORDS: Image segmentation, Image enhancement, Medical imaging, Magnetic resonance imaging, X-ray computed tomography, Image fusion, Image processing

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Proceedings Article | 23 May 2019 Paper

Paper

Thermal image stitching for examination industrial buildings

Evgenii Semenishchev, Sos Agaian, Viacheslav Voronin, Marina Pismenskova, Alexandr Zelensky, Irina Tolstova

Proceedings Volume 10993, 109930M (2019) https://doi.org/10.1117/12.2519275

KEYWORDS: Thermography, Infrared imaging, Buildings, Infrared radiation, Algorithms, Panoramic photography, Thermal imaging cameras

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Proceedings Article | 15 March 2019 Paper

Paper

A blotch detection method for archive video restoration using a neural network

Roman Sizyakin , Viacheslav Voronin, Nikolay Gapon , Marina Pismenskova , Alexey Nadykto

Proceedings Volume 11041, 110410W (2019) https://doi.org/10.1117/12.2522807

KEYWORDS: Video, Neural networks, Analog electronics, Video processing, Computing systems, Optical flow, Image processing, Intelligence systems

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Proceedings Article | 15 March 2019 Paper

Paper

Video block error recovery using modified patch-based inpainting

Nikolay Gapon, Viacheslav Voronin, Roman Sizyakin, Marina Pismenskova, Alexey Nadykto

Proceedings Volume 11041, 110410D (2019) https://doi.org/10.1117/12.2522808

KEYWORDS: Video, Image restoration, Neural networks, Video coding, Image processing, Image compression

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Proceedings Article | 17 October 2018 Presentation + Paper

Presentation + Paper

Action recognition using the 3D dense microblock difference

V. Voronin, M. Pismenskova, A. Zelensky, Yigang Cen, A. Nadykto, K. Egiazarian

Proceedings Volume 10802, 108020O (2018) https://doi.org/10.1117/12.2326801

KEYWORDS: Video, 3D image processing, Video surveillance, Fourier transforms, Detection and tracking algorithms, Feature extraction

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Proceedings Article | 26 May 2016 Paper

Paper

The algorithm stitching for medical imaging

E. Semenishchev, V. Marchuk, V. Voronin, M. Pismenskova, I. Tolstova, I. Svirin

Proceedings Volume 9870, 98700M (2016) https://doi.org/10.1117/12.2224422

KEYWORDS: Medical imaging, X-ray imaging, Image quality, Particles, Laparoscopy, Ophthalmology, Distortion, X-rays, Image segmentation, Panoramic photography

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Proceedings Article | 19 May 2016 Paper

Paper

Automatic image cracks detection and removal on mobile devices

V. Voronin, V. Marchuk, R. Sizyakin, N. Gapon, M. Pismenskova, S. Tokareva

Proceedings Volume 9869, 98690R (2016) https://doi.org/10.1117/12.2224339

KEYWORDS: Image processing, Image restoration, Mobile devices, Photography, Detection and tracking algorithms, Binary data, Defect detection, Image processing algorithms and systems, Image segmentation, Reconstruction algorithms

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Proceedings Article | 12 March 2015 Paper

Paper

Stitching algorithm of the images acquired from different points of fixation

E. Semenishchev, V. Voronin, V. Marchuk, M. Pismenskova

Proceedings Volume 9404, 94040R (2015) https://doi.org/10.1117/12.2083335

KEYWORDS: Image processing, Detection and tracking algorithms, Distortion, Sensors, Mathematical modeling, Cameras, Machine vision, Computer vision technology, Image analysis, RGB color model

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Showing 5 of 53 publications

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