This paper discusses the main scientific results of the XXV internationalconference.on Data Analytics and Management in Data-Intensive DomainsDomains, held on October 24-27, 2022 in Moscow, Russia. The motivation and...
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This paper discusses the main scientific results of the XXV internationalconference.on Data Analytics and Management in Data-Intensive DomainsDomains, held on October 24-27, 2022 in Moscow, Russia. The motivation and goals of the conference. main areas of focus, related conference., and associated events are considered. The program of the current year's conference.is described, including the topics of invited and sectional reports. Conclusions are drawn based on the results of the analysis of the scientific contribution of the conference. A list of papers selected by the conference.program committee for the special issue of the journal is attached. The papers relate to the areas of image analysis and processing, the application of machine learning methods in medicine, astronomy, materials science, and extracting information from texts.
Hyperspectral object detection (HOD) aims to identify and locate multiple objects in a scene using hyperspectral images (HSIs). While much research has focused on hyperspectral target detection (HTD) at the pixel leve...
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ISBN:
(纸本)9798400707032
Hyperspectral object detection (HOD) aims to identify and locate multiple objects in a scene using hyperspectral images (HSIs). While much research has focused on hyperspectral target detection (HTD) at the pixel level, HOD remains underexplored. Traditional HTD methods rely heavily on prior spectral information of the target and simple pixel neighborhood relationships, leading to accuracy issues when targets are occluded. Inspired by advances in RGB image detection, we propose a compact and efficient cloud-robust hyperspectral object detection network (CR-HODNet) using 3D convolution to extract spatial and spectral features jointly. We further enhance these features with channel and spatial attention mechanisms and address cloud occlusion challenges using transformer-based multi-head attention. Our method is validated on real airborne hyperspectral images with synthetic cloud occlusion, showing robust performance in challenging scenarios.
The security of image information is becoming a growing concern, image encryption technology is becoming more and more comprehensive. The algorithm encrypts the image chunks, decoding and operation mode of each image ...
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ISBN:
(纸本)9798400707032
The security of image information is becoming a growing concern, image encryption technology is becoming more and more comprehensive. The algorithm encrypts the image chunks, decoding and operation mode of each image block. It cannot resist clipping attacks, this paper increases the number of chaotic systems in the algorithm and optimizes the encryption process. The results show that the key capacity of the improved encryption algorithm is increased to 10(127), which can resist the exhaustive key attack. After encryption, the correlation of adjacent elements of the image is reduced to 10(3) orders of magnitude, and The encrypted image is crop-resistant.
The accuracy of the output data of inertial navigation system is closely related to temperature, so how to ensure the accuracy, stability and reliability of temperature acquisition is very important for inertial navig...
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ISBN:
(纸本)9798400707032
The accuracy of the output data of inertial navigation system is closely related to temperature, so how to ensure the accuracy, stability and reliability of temperature acquisition is very important for inertial navigation system. Based on engineering practice and analysis and comparison of common temperature processing software methods, this paper proposes a smoothing algorithm for establishing temperature reference, which can eliminate possible extreme values and ensure that the temperature change rate is within the expected range, further reduce temperature fluctuations, and improve the accuracy of navigation operations and the stability of subsequent navigation operations.
Addressing issues such as low contrast, blurred flames and smoke, as well as forest fog interference in low-light forest fire images, an image enhancement approach based on an optimized MSRCP algorithm is proposed. Th...
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ISBN:
(纸本)9798400707032
Addressing issues such as low contrast, blurred flames and smoke, as well as forest fog interference in low-light forest fire images, an image enhancement approach based on an optimized MSRCP algorithm is proposed. This method first converts the original image from the RGB color space to the HSV color space, and applies a weighted MSR operation to the Value (V) component. Secondly, a piecewise Gamma correction is employed to adjust the brightness of the MSR-processed V component. Finally, the fused image is remapped back to the RGB color domain, and the CLAHE algorithm is utilized to further enhance the image contrast. Experimental results demonstrate that the improved algorithm effectively enhances the clarity of flames, smoke, and objects obscured by forest fog. Furthermore, it mitigates the common issues of overexposure and underexposure encountered in MSRCP processing. Compared to the MSRCP algorithm, the improved algorithm achieves enhancements of 2.19%, 22.44%, and 2.06% respectively in the metrics of average gradient, edge strength, and information entropy.
The paper discusses the main scientific results of the XXIV internationalconference."Data Analytics and Management in Data Intensive Domains" DAMDID/RCDL-2022, held on October 4-7, 2022, in St. Petersburg, ...
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The paper discusses the main scientific results of the XXIV internationalconference."Data Analytics and Management in Data Intensive Domains" DAMDID/RCDL-2022, held on October 4-7, 2022, in St. Petersburg, Russia. A historical review of the development of the conference.was carried out. Motivation, goals of the conference. main areas of the subject, related conference., and held associated events are considered. The conference.program of the current year is characterized, including the topics of invited and sectional reports. Conclusions are drawn on the basis of the results of the analysis of the scientific contribution of the conference. Attached is a list of papers selected by the conference.program committee for a special issue of the journal. The papers relate to areas of data integration, data analysis in astronomy and biomedicine, machine learning applications, image analysis and processing, and information extraction from texts.
Feature point detection algorithms have been widely used in the fields of object recognition, panorama stitching, and robot navigation. SIFT algorithm is a robust feature detection method widely used in image processi...
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To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm comb...
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ISBN:
(纸本)9798400707032
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. This algorithm is based on the Densely Connected Convolutional Networks (DenseNet) structure of deep neural networks, and constructs a network model by introducing attention mechanisms, and trains the enhanced dataset using multi-level transfer learning. Experimental results demonstrate that the algorithm achieves an efficiency of over 84.0% in the test set, with a significantly improved classification accuracy compared to previous models, making it applicable to medical breast cancer detection tasks.
In this study, a deep learning-based galaxy classification method is discussed. The classification of galaxies is important for understanding the formation and evolution of the universe, and traditional classification...
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ISBN:
(纸本)9798400707032
In this study, a deep learning-based galaxy classification method is discussed. The classification of galaxies is important for understanding the formation and evolution of the universe, and traditional classification methods rely on artificial visual analysis, but in the face of large amounts of data, this method is time-consuming and prone to error. In recent years, automated classification methods, especially using deep learning techniques, have gradually come into focus. Deep learning models, particularly convolutional neural networks (CNNS), are capable of automatically extracting and learning complex features in galactic images, enabling efficient and accurate classification. The research plan is to integrate multimodal data, train models with large-scale datasets, and introduce interpretative analysis into the classification process to improve model transparency. Ultimately, the goal is to develop an efficient galactic classification system to support data processing and analysis in the field of astronomy.
Palmprint recognition serves as a biometric identification technology, involving the analysis and comparison of ridge patterns on an individual's palm for identity verification or individual *** technique relies o...
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