Dr. Vladimir A. Knyaz Profile

Dr. Vladimir A. Knyaz

Head of Dept. at GosNIIAS

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Author

Publications (21)

Proceedings Article | 20 June 2021 Presentation + Paper

Accuracy assessment of optical 3D measurements in hydrodynamic tunnel

Vladimir Knyaz, Mikhail Novikov, Evgeny Ippolitov

Proceedings Volume 11782, 117821G (2021) https://doi.org/10.1117/12.2592622

KEYWORDS: Calibration, 3D metrology, Environmental sensing, Accuracy assessment, Interfaces, Imaging systems, Cameras, 3D modeling, Singular optics, Refraction

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Hydrodynamic tunnel is an effective mean for studying wing flow process in aerodynamics and hydrodynamics. It allows to study flow characteristics in controlled conditions and to model the conditions that could not be studied in real flight, such as aerodynamic characteristics at critical angles of attack, in icing conditions etc. Techniques for flow visualisation such as coloured jets or small particles allow to have a qualitative data about flow behaviour, being the valuable means for understanding flow behaviour. But it is more important to have quantitative characteristics of the flow allowing to predict the process evolution and to develop safety measures and recommendations. The presented study addresses to developing a system for optical 3D measurements in hydrodynamic tunnel basing on photogrammetric techniques. To provide accurate measurements in condition of two optical media interfaces (air-glass and air-liquid) the accurate model of image formation accounting refraction is developed. The developed photogrammetric system includes several high speed cameras (from 2 to 4 cameras) mounted in a fixed position relatively the working space and a structured light projector. Original technique is applied for the system calibration. Two metrics has been used as a measure of the accuracy of the calibration: the first one being based on the test field points measurements, and the second one using points-to points distance for the surfaces of a reference object. The key contributions of this paper are: (1) accurate model of image formation in case of several media interfaces (2) a technique for photogrammetric system calibration for 3D measurements in hydrodynamic tunnel (3) experimental evaluation of calibration accuracy for multi-media 3D measurements. The performed experimental evaluation of the developed photogrammetric system has proved high accuracy of system calibration and optical 3D measurements in multi-media optical environment. The developed technique for photogrammetric system calibration and 3D measurements demonstrated applicability for the task of 3D flow analysis in hydrodynamic tunnel.

Proceedings Article | 20 June 2021 Presentation + Paper

Deep learning for 3D scene reconstruction and segmentation from stereo images

Vladimir Kniaz, Lev Grodzitsky, Vladimir Knyaz, Evgeny Ippolitov, Mikhail Novikov, Petr Moshkantsev

Proceedings Volume 11785, 117850I (2021) https://doi.org/10.1117/12.2592648

KEYWORDS: 3D modeling, Image segmentation, 3D image processing, 3D image reconstruction, Visual process modeling, Systems modeling, 3D metrology, Performance modeling, Clouds, Neural networks

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Proceedings Article | 21 September 2020 Presentation + Paper

Object recognition for UAV navigation in complex environment

V. Kniaz, V. Knyaz

Proceedings Volume 11533, 115330P (2020) https://doi.org/10.1117/12.2574078

KEYWORDS: 3D modeling, Unmanned aerial vehicles, Image segmentation, Object recognition, 3D image processing, Neural networks, Motion models, Data modeling, Data processing, Computing systems

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Impressive progress in technical characteristics of modern unmanned aerial vehicles (UAV) provides new opportunities for their exploiting in different applications and missions which were impossible earlier. The growing applicability of UAVs is based on high performance of modern computers and latest advances in sensor data processing techniques. Recent decades modern convolutional neural network (CNN) models have demonstrated the state of the art performance in many computer vision problems seemed to be solved properly only by a human. The study is aimed at developing a deep learning techniques for UAV autonomous navigation in complex environment in obstacle avoidance mode. Such kind of navigation is required for cargo delivery or rescue mission in urban, industrial or forestry environment when global geo-positioning system can be unavailable. For navigating in complex environment UAV have to recognize objects of observed scene and to estimate distance for possible obstacle. The proposed technique to solve these tasks exploits deep learning approach for image segmentation and depth map estimation using an image of the observed scene. The convolutional neural network model is developed capable to predict depth map of the observed scene along with scene segmentation according the predefined object classes. The proposed neural network architecture is based on generative adversarial model with generative part translating an input color image into an output voxel model. The aim of the discriminative part is to estimate how close the output to real data and to penalize false output. Both generative and discriminative parts are trained simultaneously on the specially prepared dataset. Evaluation on the testing part of the prepared dataset has demonstrated the ability of the developed neural network model to perform segmentation of unobserved complex scenes containing several objects and estimating depth map for this scene. The proposed neural network architecture provides high generalization ability for new scenes.

Proceedings Article | 20 September 2020 Poster + Paper

Study of the hidden ancient anthropogenic landscapes using digital models of microtopography

T. Skrypitsyna, V. Kurkov, D. Zhuravlev, V. Knyaz, A. Batasova

Proceedings Volume 11533, 115331F (2020) https://doi.org/10.1117/12.2572995

KEYWORDS: Clouds, Systems modeling, Roads, Geodesy, Image processing, Agriculture, Chemical analysis, Statistical analysis, Soil science, Chemical elements

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Proceedings Article | 6 April 2020 Presentation + Paper

Machine learning for scene 3D reconstruction using a single image

Vladimir Knyaz

Proceedings Volume 11353, 1135321 (2020) https://doi.org/10.1117/12.2556122

KEYWORDS: 3D modeling, Data modeling, 3D image processing, 3D image reconstruction, Cameras, Image processing, Solid modeling, Image segmentation, Machine learning, Convolutional neural networks

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Image-based scene 3D reconstruction is one of the key tasks for many machine vision applications such as scene understanding, object pose estimation, autonomous navigation. A set of reliable and accurate methods for multi-view scene 3D reconstruction has been developed last decades. But a significant drawback of such 3D reconstruction technique is the need for acquiring a large number of images in the processed sequence to obtain an acceptable 3D scene representation. Recently modern convolutional neural network (CNN) models achieve the best quality for object recognition, image segmentation, image translation and some other challenging computer vision problems. The paper proposes a convolutional neural network architecture and a technique for training data preparation which provide a prediction of voxel model of a 3D scene with several objects. For CNN training and evaluation a special dataset was collected and annotated. It contains image sequences of several scenes and corresponding depth images and 3D models of these scenes. The image sequence serves as the primary data used for further scene 3D reconstruction by SfM technique. Structure from Motion processing results in surface 3D models of all objects in the scene and camera positions and orientation for every image in a sequence. Then surface 3D model is transformed into voxel 3D model and segmented into separate objects. Conditional generative adversarial network architecture was developed for 3D reconstruction by single image. Its generative part translates an input color image into an output voxel model. The discriminative part distinguishes the correct output (close to real voxel model) from false output (wrong output voxel model). Both parts are trained simultaneously on the prepared dataset. Evaluation on the testing part of the prepared dataset has demonstrated the ability of prediction 3D models of previously unobserved complex scenes containing several objects. The proposed neural network architecture provides high generalization ability and improved resolution of predicted voxel 3D models.

Proceedings Article | 7 October 2019 Paper

Recognition of low-resolution objects in remote sensing images

Vladimir Knyaz

Proceedings Volume 11155, 111551X (2019) https://doi.org/10.1117/12.2533315

KEYWORDS: Remote sensing, Object recognition, Image resolution, Unmanned aerial vehicles, Data analysis, Convolutional neural networks, Cameras, Computer security, Model-based design, Machine learning

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Proceedings Article | 21 June 2019 Paper

Multimodal data fusion for object recognition

Vladimir Knyaz

Proceedings Volume 11059, 110590P (2019) https://doi.org/10.1117/12.2526067

KEYWORDS: 3D modeling, RGB color model, Cameras, Object recognition, 3D image processing, Thermal modeling, Data fusion, Thermography, Image fusion, Data modeling

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Proceedings Article | 9 October 2018 Paper

Deep learning performance for digital terrain model generation

Vladimir Knyaz

Proceedings Volume 10789, 107890X (2018) https://doi.org/10.1117/12.2325768

KEYWORDS: Machine learning, 3D modeling, Data modeling, 3D image processing, 3D image reconstruction, Performance modeling, Unmanned aerial vehicles, 3D scanning, 3D acquisition, Digital imaging

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Proceedings Article | 24 May 2018 Paper

Deep learning object recognition in multi-spectral UAV imagery

Vladimir Knyaz, Sergey Zheltov

Proceedings Volume 10679, 1067920 (2018) https://doi.org/10.1117/12.2307661

KEYWORDS: Image fusion, 3D modeling, Data fusion, Object recognition, Unmanned aerial vehicles, 3D image processing, Visible radiation, Forward looking infrared, Cameras, Data modeling

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Proceedings Article | 10 October 2017 Paper

Multisource data fusion for documenting archaeological sites

Alexander Chibunichev, Denis Zhuravlev, Vladimir Knyaz

Proceedings Volume 10427, 104271V (2017) https://doi.org/10.1117/12.2278736

KEYWORDS: 3D modeling, Data modeling, 3D image processing, 3D acquisition, Data fusion, Unmanned aerial vehicles, Data acquisition, Digital cameras, Calibration, Information visualization

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Proceedings Article | 26 June 2017 Paper

Accuracy evaluation of structure from motion surface 3D reconstruction

Sergey Zheltov, Vladimir Knyaz

Proceedings Volume 10332, 103320P (2017) https://doi.org/10.1117/12.2272021

KEYWORDS: Unmanned ground vehicles, Optical flow, Optical tracking, Motion measurement, Detection and tracking algorithms, 3D image processing, 3D modeling, 3D image reconstruction, Image processing, 3D metrology, Reconstruction algorithms, Calibration, Image registration, Cameras, 3D scanning

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Proceedings Article | 26 June 2017 Paper

Pedestrian detection in video surveillance using fully convolutional YOLO neural network

B. Vishnyakov, V. Molchanov, Y. Vizilter, O. Vishnyakova, V. Knyaz

Proceedings Volume 10334, 103340Q (2017) https://doi.org/10.1117/12.2270326

KEYWORDS: Video surveillance, Neural networks, Image processing, Object recognition, Detection and tracking algorithms, Systems modeling, Image fusion, Convolution, Video, Surveillance systems, Network architectures, Data modeling, Transform theory, Neurons, Modeling

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Proceedings Article | 5 May 2017 Presentation + Paper

Semantic image segmentation for information presentation in enhanced vision

Oleg Vygolov, Vladimir Gorbatsevich, Nikita Kostromov, Maxim Lebedev, Yury Vizilter, Vladimir Knyaz, Sergey Zheltov

Proceedings Volume 10197, 101970H (2017) https://doi.org/10.1117/12.2262507

KEYWORDS: Long wavelength infrared, Visualization, Short wave infrared radiation, Image segmentation, Image sensors, Enhanced vision, Sensors, Situational awareness sensors, Neural networks, Visibility, Infrared sensors, Image fusion, Multispectral imaging

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Proceedings Article | 17 May 2016 Paper

Multispectral image fusion based on diffusion morphology for enhanced vision applications

Vladimir Knyaz, Oleg Vygolov, Yury Vizilter, Sergey Zheltov, Boris Vishnyakov

Proceedings Volume 9840, 984022 (2016) https://doi.org/10.1117/12.2224086

KEYWORDS: Image fusion, Diffusion, Enhanced vision, Image segmentation, Multispectral imaging, Image quality, Image analysis, Algorithm development, Control systems, Prototyping

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Proceedings Article | 29 April 2016 Paper

Robust object tracking techniques for vision-based 3D motion analysis applications

Sergey Zheltov, Boris Vishnyakov, Vladimir Knyaz

Proceedings Volume 9896, 98961D (2016) https://doi.org/10.1117/12.2227879

KEYWORDS: Algorithm development, Algorithm development, Detection and tracking algorithms, 3D image processing, Motion analysis, Calibration, Reliability, 3D acquisition, Cameras, Video, 3D metrology, Imaging systems

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Proceedings Article | 22 June 2015 Paper

Stereo sequences analysis for dynamic scene understanding in a driver assistance system

Boris Vishnyakov, Yuri Vizilter, Vladimir Knyaz, Ivan Malin, Sergey Zheltov, Oleg Vygolov

Proceedings Volume 9530, 95300P (2015) https://doi.org/10.1117/12.2184849

KEYWORDS: Roads, 3D modeling, Detection and tracking algorithms, Calibration, Cameras, Orthophoto maps, Image segmentation, Imaging systems, Video, Modeling

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Proceedings Article | 23 May 2013 Paper

Technique for real-time frontal face image acquisition using stereo system

Vladimir Knyaz, Yuri Vizilter, Yuri Kudryashov

Proceedings Volume 8791, 87910U (2013) https://doi.org/10.1117/12.2021006

KEYWORDS: 3D modeling, Cameras, Reconstruction algorithms, 3D image processing, Imaging systems, Computer programming, Facial recognition systems, Image quality, Systems modeling, 3D image reconstruction

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Proceedings Article | 30 April 2002 Paper

Photogrammetry for rapid prototyping: development of noncontact 3D reconstruction technologies

Vladimir Knyaz

Proceedings Volume 4644, (2002) https://doi.org/10.1117/12.464174

KEYWORDS: 3D modeling, 3D metrology, Cameras, Reverse modeling, Rapid manufacturing, Calibration, 3D acquisition, Image processing, Image acquisition, Structured light

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Proceedings Article | 8 March 2002 Paper

Virtual face reconstruction based on 3D skull model

Sergey Zheltov, Elena Saltykova, Dmitry Stepanyants, Vladimir Knyaz

Proceedings Volume 4661, (2002) https://doi.org/10.1117/12.460172

KEYWORDS: 3D modeling, Skull, 3D image processing, Volume rendering, Nose, Tissues, Eye models, Photography, 3D scanning, Visual process modeling

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Proceedings Article | 12 February 2001 Paper

Method for 3D noncontact measurements of cut trees package area

Vladimir Knyaz, Yuri Vizilter

Proceedings Volume 4189, (2001) https://doi.org/10.1117/12.417202

KEYWORDS: 3D metrology, Image processing, Imaging systems, Cameras, Calibration, CCD cameras, Frame grabbers, 3D image processing, Image acquisition, Software development

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Proceedings Article | 22 December 2000 Paper

Automated photogrammetric system for photorealistic skull 3D reconstruction

Sergey Zheltov, Dmitry Stepanyants, Vladimir Knyaz

Proceedings Volume 4309, (2000) https://doi.org/10.1117/12.410892

KEYWORDS: 3D modeling, Skull, Cameras, 3D image processing, Calibration, Projection systems, 3D metrology, Image acquisition, CCD cameras, Structured light

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Showing 5 of 21 publications

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