The article is devoted to the achievements of the leading scientific school of Academician v.A. Soifer in the field of biomedical imageprocessing. The main stages of development of research in the field of analysis o...
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The article is devoted to the achievements of the leading scientific school of Academician v.A. Soifer in the field of biomedical imageprocessing. The main stages of development of research in the field of analysis of medical data are given. various tasks in processing, analysis, and recognition of medical images, as well as their specifics, are considered. Methods, algorithms, and systems obtained during joint research with major medical institutions in the Russian Federation are described.
Subject of study. Results of comparative analysis and testing of the application capabilities of several neural network detection algorithms, programming interfaces, and machine learning libraries for real-time analys...
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Subject of study. Results of comparative analysis and testing of the application capabilities of several neural network detection algorithms, programming interfaces, and machine learning libraries for real-time analysis of graphical data from scanning thermal imaging surveying systems are presented. Method. The availability of programming interfaces for integration and adaptation of algorithms into the developed software, data processing rate, and object detection accuracy were selected as the main criteria for assessing the detection algorithms. These criteria were evaluated using practical experiments involving training and running neural network algorithms on test software using computers with different configurations. Main results. Modern neural network algorithms were demon-strated to enable image data processing with detection accuracy for specified object classes, which is sufficient for the automation of image recognition aimed at real-time processing of images from scanning thermal imaging surveying systems. Practical significance. The results of the investigation and tests presented in this study can be advantageous and reduce the time required for developers to find base neural network algorithms suitable for a practical implementation of automation aimed at imageprocessing. Implementation of the considered algorithms in the developed software enables image analysis and processing in real time during surveillance owing to a reduc-tion in the amount of data processed by the operator, thus enabling the removal of the post-processing stage from the technological sequence of the surveillance.(c) 2023 Optica Publishing Group
The task of detecting and identifying low contrast objects by thermal imaging optoelectronic systems in a scene with a large dynamic range requires the use of special brightness conversion algorithms. However, the mos...
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The task of detecting and identifying low contrast objects by thermal imaging optoelectronic systems in a scene with a large dynamic range requires the use of special brightness conversion algorithms. However, the most popular and high-quality methods, such as digital detail enhancement (DDE), introduce large frame delays and require significant hardware resources. This article presents a review of dynamic range enhancement algorithms among which algorithms that do not require a large number of FPGA logic elements and allow for minimal frame delay. Based on them, developed mathematical models of gradational transformation of brightness, which can detect low-contrast details of the image. The results of their implementation on FPGA as a part of a domestic optoelectronic module are given.
This paper proposes an approach for detecting smoke in industrial production using computer vision. The task of detecting smoke and fire can be framed as a detection problem, making modern convolutional neural network...
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This paper proposes an approach for detecting smoke in industrial production using computer vision. The task of detecting smoke and fire can be framed as a detection problem, making modern convolutional neural network models well-suited for this task. The main issues of detection in industrial production are considered, and solutions to these problems are proposed. In the study, the Faster R-CNN, MobileNet SSD v2, and YOLOv8 models were trained and tested in combination with various image preprocessingalgorithms. The best result was achieved by the YOLOv8 model combined with the adaptive histogram equalization algorithm for image preprocessing, showing a precision value of 80.1%. As a result, it was demonstrated that deep convolutional networks are well-suited for the task of detecting smoke and fire. Additionally, the main problems and solutions for preparing data for training deep convolutional models were explored.
The study uses phase triangulation methods to examine the development of imageprocessingalgorithms for measuring a three-dimensional (3D) surface profile. An algorithm is proposed for decoding images of an object in...
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The study uses phase triangulation methods to examine the development of imageprocessingalgorithms for measuring a three-dimensional (3D) surface profile. An algorithm is proposed for decoding images of an object in structured light based on an iterative search for the minimum deviation of the model function using measurement results and compensating for the nonlinearity of the transceiver path of the measuring complex. A distinctive feature of the proposed method is that the search for the model function is desired in the form of a second-degree polynomial, ensuring the stability of the method against nonlinear distortions caused by power-law transformations of the transceiver path. The interval search method is used to reduce algorithmic complexity qualitatively. The proposed algorithm provides a stable search for the value of the initial phase shift in object images, is resistant to noise and nonlinear distortions, and allows imageprocessing in 3D scanning systems based on triangulation methods using structured illumination and phase triangulation. Thus, the algorithm will be useful for data processing during the operation of measuring systems with a nonlinear transceiver path and measurement time constraints.
This article presents an intelligent deployment solution for tabling that utilizes deep learning techniques. The solution involves adapting deep learning semantic segmentation algorithms with DeepLab v3+ to extract mu...
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This article presents an intelligent deployment solution for tabling that utilizes deep learning techniques. The solution involves adapting deep learning semantic segmentation algorithms with DeepLab v3+ to extract multi-dimensional image features, enabling the mapping of relationships between mineral ore belt characteristics and operating parameters using a multi-output support vector regression model optimized using a sparrow search algorithm (ssa-msvr). The proposed solution integrates image recognition software and data processing method, which significantly improves the efficiency and effectiveness of mineral processing, providing a promising avenue for further research and development in this field.
In this study, an ad-hoc imageprocessing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian ...
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In this study, an ad-hoc imageprocessing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian Mixture Model (GMM), characterized as a soft clustering method, has been employed for this task, yielding noteworthy results in both kernel and germ segmentation. A comparative analysis was conducted, wherein GMM was compared with two hard clustering methods, hierarchical clustering and k-means, as well as other common clustering algorithms prevalent in food HSI applications. Notably, GMM exhibited the highest accuracy, with a Jaccard index of 0.745, surpassing hierarchical clustering at 0.698 and k-means at 0.652. Furthermore, the spectral variations observed in wheat kernel topology can be used for semantic image segmentation, especially in the context of selecting the germ portion within the wheat kernels. These findings carry practical significance for professionals in the fields of hyperspectral imaging (HSI) and machine vision, particularly for food product quality assessment and real-time inspection.
The ability of Advanced Driving Assistance systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today's vehicles are e...
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The ability of Advanced Driving Assistance systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today's vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%.
The problem of improving the efficiency of decision making based on cases (case-based reasoning) in real-time intelligent systems is considered. Methods for preprocessing and storing temporal data are discussed. A met...
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The problem of improving the efficiency of decision making based on cases (case-based reasoning) in real-time intelligent systems is considered. Methods for preprocessing and storing temporal data are discussed. A method and algorithms for structuring the case base are proposed. The main stages of the method are the generalization of properties of dynamic parameters, the formation of classes of similar situations associated with cases from the case base, and the construction of decision trees to enable efficient search for solutions in each class. The results of a machine experiment are presented. To implement the proposed approach, a temporal database created using the Neo4j NoSQL graph database management system is used.
Deep learning has been widely used in medical imageprocessing, which has sparked the development of a wide range of applications and led to a notable increase in the number of therapeutic and diagnostic options avail...
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Deep learning has been widely used in medical imageprocessing, which has sparked the development of a wide range of applications and led to a notable increase in the number of therapeutic and diagnostic options available for a range of medical imaging problems. In the era of the Internet of Things (IoT), safeguarding the security and privacy of medical data is crucial to the advancement of sophisticated diagnostic applications for medical imaging. Deep learning-based brain tumor detection in smart health care systems with privacy preservation is proposed in this paper. The system under consideration is organized into three discrete stages that are then combined to provide an all-encompassing blueprint. During the first phase, patients with brain tumors are the primary target of an efficient healthcare system that is introduced. A Microsoft-based operating system-compatible application has been developed to accomplish this. Patient data is secure and only available to the hospital and the individual patient, which enables patients to engage with the system both locally and virtually. To obtain the anticipated outcomes, the user must first submit the patient's MRI scan and then enter a special 10-digit code. In the second part, the authors develop a deep learning-based tumor identification platform which also incorporates the AES-128 algorithms and PBKDF2 for secure medical image storage on the server and data transmission via the internet from the client to the server and back to the client upon prediction. The proposed approach integrates ResNet-50, Inception v3, and vGG-16 architecture to build a Convolutional Neural Network (CNN)-based brain tumor diagnosis system. These architectures are enhanced through significant pre-processing, SGD, RMSprop, and Adam optimization. Our research focuses on the application of cutting-edge methods to maintain confidentiality and accomplish precise tumor diagnosis, underscoring the importance of privacy preservation. Our micro-av
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