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检索条件"任意字段=Conference on Applications of Digital Image Processing XXXV"

共 10159 条记录，以下是51-60 订阅

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Application of UAV, GNSS and InSAR Techniques in the Raw Material Supply Chain 11

Application of UAV, GNSS and InSAR Techniques in the Raw Mat...

引用

11th International conference on Geographical Information Systems Theory, applications and Management, GISTAM 2025

作者： Grabrijan, Tanja Oštir, Krištof Trajkovski, Klemen Kozmus Grigillo, Dejan Horvat, Veronika Grabrovec Prešeren, Polona Pavlovčič Hamza, Veton Pepe, Antonio Calò, Fabiana Falabella, Francesco Sanz-Ablanedo, Enoc Gheorghe, Mihaela Selea, Teodora Teodoro, Ana Cláudia University of Ljubljana Faculty of Civil and Geodetic Engineering Jamova cesta 2 Ljubljana Slovenia Diocleziano Napoli328 Italy Universidad de León León Spain GMV Innovating Solutions SRL Bucharest Romania Rua do Campo Alegre 687 Porto Portugal

ISBN: (纸本)9789897587412

The marble quarry, located in southern Austria produces high-quality marble, both in open-pit and underground extraction sites. Extraction, transportation and accumulation of material require close monitoring to maintain the stability of the whole area and to observe changes in waste dumps. In this study, we have shown how different methodologies can be used to support the monitoring of the entire raw material supply chain. Several unmanned aerial vehicle (UAV) surveys were performed to compute high-resolution digital elevation models (DEM) to serve as a reference for comparing stereo and tri-stereo DEMs calculated from satellite imagery. A low-cost global navigation satellite system (GNSS)-based monitoring system was set up to estimate horizontal and vertical displacements. In addition, the state-of-the-art technique of Interferometric synthetic aperture radar (InSAR) provided displacement time series for a broader area. © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Stereo image processing

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applications of Cloud Computing and Artificial Intelligence in the Oil and Gas Industry: A Review

Applications of Cloud Computing and Artificial Intelligence ...

引用

International image processing, applications and Systems conference (IPAS)

作者： Alfitouri Ibrahim Jellah Mohamed. M Moaad. I Oil and Gas Engineering Department Bani-Waleed University Bani-Waleed Libya

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper reviews the latest machine learning and artificial intelligence technology, showcasing its potential to revolutionize the oil and gas industry. Despite the current limitations, the industry is on the brink of a digital transformation that promises to enhance operations and increase overall production. With the help of AI and cloud computing, the oil and gas industry's future is one of optimism and potential. The review showed a significant increase in production when applying AI technology, particularly with EOR. The paper summarized the limitations and future challenges of this technology.

关键词： Cloud computing Reviews Oils Storage management Production Machine learning Companies Gas industry Reservoirs Petroleum industry

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Method for Assessing the Efficiency of Space Debris image Formation and processing by Orbital Computer Vision Systems

Method for Assessing the Efficiency of Space Debris Image Fo...

引用

digital Signal processing and its applications (DSPA), International conference on

作者： Ruslan S. Siryi Pavel S. Baranov Department of Television Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper proposes a method for assessing the efficiency of orbital computer vision systems (OCVS) for space debris (SD) monitoring, based on the metric of the total number of detected objects with an acceptable trajectory error for a specific OCVS as part of a spacecraft in a modeled space environment. The method incorporates models of the environment, objects, as well as the hardware and software components of the OCVS. Functional dependencies are presented, demonstrating the mutual influence of various modules included in the proposed method. The application of the method is demonstrated through the comparison of different system configurations, enabling quantitative evaluation of their efficiency and the justified optimization of system parameters.

关键词： Space vehicles Computer vision Space debris Computational modeling Orbits Software Hardware Trajectory Monitoring Signal to noise ratio

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Active Contour Method Modification for Diabetic Macular Edema Area Quantification in OCT images

Active Contour Method Modification for Diabetic Macular Edem...

引用

digital Signal processing and its applications (DSPA), International conference on

作者： Egor Volkov Anastasia Linich Alexey Averkin Intellegent Systems Department FRC CSC RAS Moscow Russia Faculty of Applied Mathematics and Informatics MIPT Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

A new method for automated quantification of diabetic macular edema on optical coherence tomograms based on modification of the active contour model is proposed. Traditional active contour models require manual initialization of the contour and are computationally expensive. This paper presents a modification of the IAC that uses a fast Fourier transform to accelerate the iterative segmentation process and automatic loop initialization. The model is reformulated using a threshold function that defines the contour as a region boundary, and includes a Fourier function of the pressure force with sign to account for global image features and a gradient force for local adaptation. A spectral residual approach based on the analysis of the frequency spectrum of the image is applied for automatic initialization. The proposed method combines the advantages of the frequency approach and regional segmentation, providing automatic initialization, robustness to background inhomogeneities, and high-speed performance comparable to traditional active contour models despite processing the entire image. This modification of the active contour model allows efficient and automatic extraction of diabetic macular edema boundaries on optical coherence tomograms, eliminating the need for manual initialization and ensuring the practical applicability of the method for quantitative assessment of the diabetic macular edema area.

关键词： image segmentation Accuracy Computational modeling Force Manuals Coherence Optical imaging Diabetes Active contours High-speed optical techniques

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Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach

Low-Data Classification of Historical Music Manuscripts: A F...

引用

International image processing, applications and Systems conference (IPAS)

作者： Elona Shatri Daniel Raymond György Fazekas Centre for Digital Music Queen Mary University of London London UK Big Data Science Queen Mary University of London London UK

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional Neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques.

关键词： Training Support vector machines Scalability Symbols Music Self-supervised learning Multilayer perceptrons Feature extraction Cultural differences Few shot learning

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Flattening images Manifold at the Nishimori Point: Exploring Linearity in Polysemantic Embedding for Human-Aware Bayesian CNN image Classification

Flattening Images Manifold at the Nishimori Point: Exploring...

引用

digital Signal processing and its applications (DSPA), International conference on

作者： Vasiliy S. Usatyuk Denis A. Sapozhnikov Sergey I. Egorov R&D Department T8 LLC Moscow Russia Department of Computer Science South-West State University Kursk Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Efficient image classification often requires a balance between accuracy and computational complexity. In this paper, we propose a graph-based spectral embedding method that leverages improved Nishimori temperature estimation to achieve low-dimensional feature representations while maintaining high classification performance. Our approach reduces image features extracted by MobileNetV2 to a 32-dimensional manifold using graph embeddings, significantly decreasing the parameter count compared to conventional methods like bottleneck ResNet-18. On the imageNet10 dataset, the proposed method achieved 98% accuracy with only 32 dimensions, while on the imageNet-100 dataset, it matched the performance of a 64-dimensional bottleneck layer using half the dimensionality. Furthermore, with polysemantic multigraph embeddings exceeding 64 dimensions, the method demonstrated a 1.5% accuracy improvement. The computational efficiency, achieved by reducing parameters from 51200 to 3200, highlights the potential of this method for resource-constrained applications. These results demonstrate that the proposed graph-based embedding framework is a scalable and effective solution for image classification tasks, offering a compelling alternative to conventional deep learning architectures. Future work will extend this approach to coarsely quantized feature extracting, improved discriminators, Diffusions and Transformers neural networks.

关键词： Manifolds Temperature distribution Accuracy image coding Estimation Feature extraction Transformers Parity check codes Computational efficiency image classification

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Multi-scale Traffic Camera image Detection Network Based on Improved YOLOv8 21st

Multi-scale Traffic Camera Image Detection Network Based on...

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21st Pacific Rim International conference on Artificial Intelligence, PRICAI 2024

作者： Peng, Zhihao Qi, Xinyuan Wu, Sheng Shang, Jianga Yang, Linquan China University of Geosciences Wuhan430074 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819601240

With the extensive deployment of traffic cameras across cities, utilizing these devices for vehicle detection has become essential for efficient traffic management. However, variations in vehicle sizes and distances between vehicles and cameras lead to inconsistent scaling of vehicles in the images, which presents a challenge for feature extraction. Furthermore, conventional object detection algorithms fail to fully utilize the rich feature information across multiple layers. To solve these issues, we develop a novel and advanced multi-scale traffic YOLO (MT-YOLO), based on the YOLOv8 model. Firstly, we propose the spatial multi-scale (SMS) attention mechanism to improve the extraction capability of spatial multi-scale vehicle features and aggregate these features for dynamic spatial modulation. Secondly, we develop the multi-scale dilation transposition CARAFE (MDT-CARAFE) upsampling method, which utilizes dilated convolutions to enhance the receptive field at different scales during the upsampling process. Lastly, we augment the network with a detection head and replace the original detection head with the adaptively spatial feature fusion(ASFF) detection head, which dynamically selects and fuses multi-layer feature maps, enabling the network to better capture multi-scale information. Our experiments demonstrate that the MT-YOLO model greatly improves results on the traffic camera detection dataset., achieving an mAP@0.5 of 80.9%, with a precision of 92% and recall of 93%. These results surpass the performance of leading detection algorithms. In addition, the model takes only 12.5 milliseconds to complete an inference, which makes it highly deployable and valuable for real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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6G XL-MIMO Channel Estimation with Sub-Nyquist Tensor Completion

6G XL-MIMO Channel Estimation with Sub-Nyquist Tensor Comple...

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digital Signal processing and its applications (DSPA), International conference on

作者： Semyon Dorokhin Vladimir Lyashev Mikhail Makurin Moscow Institute of Physics and Technology Moscow Russia Radio Transmission Technology Lab Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Channel estimation overhead reduction is one of the main problems for 6G XL-MIMO systems. As the number of antennas and subcarriers grows, so does the overhead of traditional channel estimation methods. High overhead limits user mobility and affects the latency. Most of the popular tensor-based channel estimation algorithms combine reference signals transmission and channel tensor estimation into one problem. Such approach typically imposes limitations on reference signals, making backward-compatibility with existing 5G standard challenging. We propose to separate tensor completion and channel tensor elements estimation into different tasks. We show that tensor completion algorithms from image processing and fMRI scanning can be reused for sub-Nyquist completion of OFDM MIMO tensors to reduce the overhead. Furthermore, we extend one of these algorithms from CPD to Tensor Train (TT) and demonstrate that TT-based algorithm reduces channel estimation error up to 2 times. With the proposed approach other algorithms can be further developed based on tensor completion theory.

关键词： 6G mobile communication Tensors OFDM image processing Channel estimation Signal processing algorithms Estimation Massive MIMO Functional magnetic resonance imaging Standards

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Sclera Segmentation on the Half-UNet Neural Network

Sclera Segmentation on the Half-UNet Neural Network

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digital Signal processing and its applications (DSPA), International conference on

作者： Sergei Chernyadyev Natalya Kharina Department of the Radio Electronic Means Vyatka State University Kirov Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Nowadays, technology for biometric authentication technologies is actively implemented, users can usually authenticate themselves by their face, voice, fingerprint, vascular bed of the finger, palm, iris. Using images of sclera vessels for authentication is mathematically and technically more complex, so it is used less often. In addition, existing sclera segmentation algorithms often do not allow ensuring the required quality of authentication systems. The paper proposes an algorithm for sclera segmentation based on the lightweight convolutional neural network Half-UNet, and compares the obtained results with known algorithms for sclera segmentation. Analysis of the obtained results shows that the developed algorithm effectively highlights the eye sclera region, surpassing known modern algorithms, and can be used in implementation of biometric authentication system as a stage of processing scanner images before the sclera vessels segmentation stage.

关键词： Performance evaluation image segmentation Neural networks Memory management Signal processing algorithms Authentication Biometric authentication Mobile handsets System implementation Iris recognition

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Color Imaging in Medical Video Systems: Problems and Features

Color Imaging in Medical Video Systems: Problems and Feature...

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digital Signal processing and its applications (DSPA), International conference on

作者： Natalia A. Obukhova Alexandr A. Motyko Alexandr A. Pozdeev Konstantin A. Smirnov SPbSU ETU “LETI” St. Petersburg Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

This paper explores the issue of accurate colour reproduction in medical imaging. It considers key problems and lists standards related to the process of forming and displaying medical images. Particular attention is paid to the fact that the existing standards do not take into account the individual characteristics of colour perception in different doctors. In the conditions of using modern displays with wide colour coverage this can lead to errors in colour rendering for a particular doctor, which, in turn, reduces the accuracy of disease diagnostics. The application of optimal LUT (Look-Up Table) with variable resolution to recalculate the colour space of the display is proposed, which allows to personalise the image for individual features of the doctor's vision and improve the accuracy of diagnosis. Experimental results demonstrating the advantages of the proposed approach over classical colour transform methods are presented. The results confirm that the use of variable resolution LUT can reduce colour rendering errors and improve the quality of medical diagnostics.

关键词： image resolution Accuracy Three-dimensional displays image color analysis Transforms Observers Rendering (computer graphics) Table lookup Standards Signal resolution

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