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Image Haze Removal Using Block-Based Multi-Scale Enhancement Method

Image Haze Removal Using Block-Based Multi-Scale Enhancement...

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Multimodal Image Exploitation and Learning 2023

作者： Voronin, V. Tokareva, O. Gapon, N. Naumov, I. Zelensky, A. Semenishchev, E. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

An image haze removal algorithm based on multi-scale block-rooting processing has been developed for removing haze/fog from an image based on frequency-domain coefficient correction of a set of images followed by their fusion based on the Laplacian pyramid. A new stage is proposed in obtaining a local-global estimate of high-contrast images, which are also used in the general fusion model. The proposed block-based multi-scale enhancement method base on 3-D blockrooting multi-scale transform domain technique, comprising: finding similar blocks in the image by block-matching;block-grouping for different block sizes;applying 3-D block-matching parametric image enhancement;calculating the quality measure of enhancement;optimizing parameters of image enhancement method through the quality measure of enhancement;fusing different enhanced images. To test the performance of the proposed algorithm, the public database O-HAZE is used. © 2023 SPIE.

关键词： Image enhancement

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Defect Detection and Removal for Depth Map Quality Enhancement in Manufacturing with Deep Learning 12

Defect Detection and Removal for Depth Map Quality Enhanceme...

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Dimensional Optical Metrology and Inspection for Practical Applications XII 2023

作者： Gapon, N. Voronin, V. Sizyakin, R. Zhdanova, M. Semenishchev, E. Zelensky, A. Don State Technical University Rostov-on-Don Russia Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia

ISBN: (数字)9781510661639

ISBN: (纸本)9781510661622

A system for determination the distance from the robot to the scene is useful for object tracking, and 3-D reconstruction may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by incorrect sections on the depth map because of incorrect distance determination of objects. We present a two-stage approach combining defect detection and depth reconstruction algorithms. The first step is image defects detection based on convolutional auto-encoder (U-Net) and deep feature fusion network (DFFN-Net). The second step is a depth map reconstruction with the exemplar-based and the anisotropic gradient concepts. The proposed modified block fusion algorithm uses a local image descriptor obtained by an automatic encoder for image reconstruction, which extracts image features and depth maps using a decoding network. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on RGB-D benchmark for evaluating manufacturing vision systems. © 2023 SPIE.

关键词： Robots

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Action Recognition Algorithm from Multimodal Images for Human-Robot Collaboration Systems 3

Action Recognition Algorithm from Multimodal Images for Huma...

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Multimodal Sensing and Artificial Intelligence: Technologies and Applications III 2023

作者： Voronin, V. Zhdanova, M. Semenishchev, E. Zelensky, A. Gapon, N. Ilyukhin, Yu. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (纸本)9781510664517

Automation of production processes using robots is a priority for developing many industrial enterprises. Human-machine interaction is a key component of such control infrastructure. The proposed algorithm is a four-stage procedure: (a) fusion information from multimodal sensors based on the quaternion model, (b) image preprocessing using a 3D Gabor filter, (c) a descriptor calculation using 3D local binary dense micro-block difference with skeleton points, and (d) classification. The proposed algorithm is based on capturing 3D sub-volumes located inside a video sequence patch and calculating the difference in intensities between these sub-volumes;for intensified motion, used the convolution with a bank of 3D arbitrarily oriented Gabor filters and calculating 3D local binary dense micro-block difference. A program was developed for transmitting information about the target points of the robot's movement through a virtual TCP / IP port and a script for working out the target points in the simulation environment. To test the effectiveness of the proposed algorithm, we simulate an action recognition system for Human-robot collaboration in the RoboGuide environment. © 2023 SPIE.

关键词： Remote control

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Scanning Trajectory Optimization for Industrial Robot Based on the Scanned Objects Surface Optical Properties 12

Scanning Trajectory Optimization for Industrial Robot Based ...

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Dimensional Optical Metrology and Inspection for Practical Applications XII 2023

作者： Voronin, V. Gapon, N. Naumov, I. Zelensky, A. Semenishchev, E. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9781510661639

ISBN: (纸本)9781510661622

This work presents a workflow to automatically calculate optimized scanning trajectories for industrial robot system using the estimation of a surface reflectance model of 3-D shape and parameters from multiple views. To solve the problem of determining the views without Lambert’s surface, a 3-D reconstruction algorithm based on a convolutional neural network is proposed. In the first step, 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 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 RGB image. As a result, this method uses non- Lambertian properties, and it can compensate for triangulation reconstruction errors caused by view-dependent reflections. Experimental results on both synthetic and real objects show that the proposed method automatically finds trajectories that enable 3-D reconstructions, with a significant reduction of scanning time. © 2023 SPIE.

关键词： Optical properties

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Action Recognition from Thermal Videos Using Skeleton Information and 3D Local Binary Dense Micro-Block Difference

Action Recognition from Thermal Videos Using Skeleton Inform...

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Multimodal Image Exploitation and Learning 2023

作者： Voronin, V. Zhdanova, M. Gapon, N. Semenishchev, E. Zelensky, A. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

The solution to the problem of human actions recognition on video sequences is one of the key areas on the path to the development and implementation of computer vision systems in various spheres of life. Such areas as video surveillance systems, monitoring, contactless control interfaces, video processing as a preliminary stage of processing, etc. While additional sources of information (such as depth sensors, thermal sensors) allows to get more informative features, and thus increase the reliability and stability of recognition. In this work we focus on the simultaneous extraction of skeleton and 3D local binary dense micro-block difference information of an object from a visible and thermal image. The proposed algorithm is a four-stage procedure: (a) fusion information from visible cameras and thermal sensors based on the PLIP model (parameterized model of logarithmic image processing), (b) image preprocessing using a 3D Gabor filter, (c) a descriptor calculation using 3D local binary dense micro-block difference with skeleton points, and (d) classification. The proposed algorithm is based on capturing 3D sub-volumes located inside a video sequence patch and calculating the difference in intensities between these sub-volumes;for intensified motion, used the convolution with a bank of 3D arbitrarily oriented Gabor filters. We calculate the local features for pre-processed frames, such as 3D local binary dense micro-block difference (3D LBDMD). As a result of the experiments, it was shown that the method proposed in this study shows good performance compared to other state-of-the-art methods. © 2023 SPIE.

关键词： Gabor filters

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Deep Learning-Based Image Defect Detection and Removal in Manufacturing

Deep Learning-Based Image Defect Detection and Removal in Ma...

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Digital Optical Technologies 2023

作者： Gapon, N. Voronin, V. Zhdanova, M. Sizyakin, R. Semenishchev, E. Ilyukhin, Y. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9781510664586

ISBN: (纸本)9781510664579

Image inpaintnig in textile manufacturing is a new emerging research topic in preprocessing for jacquard CAD systems. One of the most important aspects of a jacquard CAD system is the simulation of the appearance of a jacquard texture during inference. Jacquard image inpainting has become an indispensable process for the Jacquard CAD system. Jacquard image reconstruction is designed to restore a damaged image with missing information, so it is necessary to determine which parts of the image need to be repaired. Thus, this task includes two processing stages: the detection of defects and their recovery. This article presents a two-stage approach that combines new and traditional algorithms for detecting defects and repairing damaged areas. The first stage is a defect detection method based on a convolutional autoencoder (U-Net). The second stage is image inpainting based on exemplar-based concepts and the anisotropic gradient. Our system quantitatively outperforms state-of-the-art methods regarding reconstruction accuracy in the benchmark. © 2023 SPIE.

关键词： Image reconstruction

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Block-Based Multi-Scale Image Enhancement Method for Industrial Inspection System 13

Block-Based Multi-Scale Image Enhancement Method for Industr...

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Optical Measurement Systems for Industrial Inspection XIII 2023

作者： Voronin, V. Gapon, N. Zhdanova, M. Semenishchev, E. Zelensky, A. Ilyukhin, Yu. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9781510664463

ISBN: (纸本)9781510664456

Automated inspections and intelligent image processing optimize quality control processes. Images captured in the industrial inspection system have low contrast and faint color. Many enhancement algorithms have recently been proposed to enhance visibility and restore color. We present a new image enhancement algorithm based on multi-scale block-rooting processing. The proposed method based on the frequency-domain coefficient correction of a set of images followed by their fusion based on the Laplacian pyramid. A new stage is presented in obtaining a local-global estimate of high-contrast images, also used in the general fusion model. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than in the original image. The experiment results on the test dataset confirmed the high efficiency of the proposed enhancement method compared to the state-of-the-art techniques for industrial inspection systems. © 2023 SPIE.

关键词： Image enhancement

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Image Inpainting With Deep Adaptive Generative Models for Multimedia Computing 10

Image Inpainting With Deep Adaptive Generative Models for Mu...

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Optoelectronic Imaging and Multimedia technology X 2023

作者： Voronin, V. Gapon, N. Zhdanova, M. Tokareva, O. Semenishchev, E. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (纸本)9781510667839

Image inpainting is a challenging task where large missing regions must be filled based on the available data. It is an interesting new research topic in multimedia computing and image processing. This paper proposes a novel deep learning reconstruction method using an adaptive generative adversarial neural network (aGAN). The key feature of the proposed network is the dynamic estimation of the trend of the loss function. This estimate finds the optimal balance between adversarial and pixel-by-pixel losses. We introduced the coefficient of the approximating curve, which allows us to estimate the trend dynamics of the loss function. Depending on the value of this curve, we adjust the weight of the pixel-by-pixel loss to make the restored area sharper. The proposed method allows a higher sharpness of the reconstructed area and a more stable learning process. Experiments on the dataset show that our method successfully predicts multimedia information in missing regions and significantly outperforms the state-of-the-art methods. © 2023 SPIE. All rights reserved.

关键词： Pixels

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Visible Watermarks Inpainting Using Deep Learning for Multimedia Application 10

Visible Watermarks Inpainting Using Deep Learning for Multim...

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Optoelectronic Imaging and Multimedia technology X 2023

作者： Gapon, N. Yadrets, E. Voronin, V. Sosnovsky, A. Zhdanova, M. Semenishchev, E. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia

ISBN: (纸本)9781510667839

In recent decades, with the development of digital technologies and the spread of the Internet, photography has become an integral part of our daily lives. Often, images with objects superimposed in text, symbols, or drawings. These superimposed objects can serve a variety of purposes. Adding text or graphics can highlight specific details in an image or convey important information, such as the event's date, location, or specifics. Adding graphic elements may be used commercially to promote products, services, or a brand. Another area of application of such objects is the field of intellectual property protection or verification of the authenticity of a digital image. The problem with such images is that objects can completely overlap the image, leading to a distortion of the perceived information. In this article, we propose an effective convolutional autoencoder model for removing watermarking and restoring an image's usable part. This network can perfectly remove the embedded, visible watermark, restoring the image. Experimental results demonstrate the effectiveness and superiority of our scheme. © 2023 SPIE. All rights reserved.

关键词： Image reconstruction

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Real-Time cognitive Algorithm for Adapting the Actions of MultiPurpose Robotic Systems 11

Real-Time Cognitive Algorithm for Adapting the Actions of Mu...

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Optoelectronic Imaging and Multimedia technology XI 2024

作者： Voronin, V. Khamidullin, I. Semenishchev, E. Zhdanova, M. Gapon, N. Kazaryan, M. Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Don State Technical University Rostov-on-Don Russia North Ossetian State Medical Academy Vladikavkaz Russia

ISBN: (纸本)9781510682061

Industry 4.0 marks a shift toward fully automated digital production, where intelligent systems manage processes in real-time and interact continuously with their environment. Central to this evolution is robotic technology, which enhances productivity and precision in manufacturing. A key aspect of this advanced production model is human-robot interaction, where operators and robots work together on complex tasks. Ensuring safe collaboration between humans and robots is a primary objective. This paper proposes a method for human gesture recognition based on multi-sensor data fusion. By incorporating data from multiple sensors, we achieve a more complete and robust representation of gestures. Our approach involves an algorithm that classifies human movements in real-time using visual data. The process consists of several steps: data preprocessing, feature extraction, data integration, and gesture classification. By employing machine learning and deep learning techniques for feature extraction and analysis, we aim to achieve high accuracy in recognizing gestures. © 2024 SPIE.

关键词： Deep learning

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