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检索条件"任意字段=18th Image Processing: Algorithms and Systems Conference, IPAS 2020"

共 258 条记录，以下是11-20 订阅

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Application of computer vision methods for automated wooden planks length measurement 18

Application of computer vision methods for automated wooden ...

引用

18th Baltic Electronics conference

作者： Zabulis, Lukas Lipnickas, Arunas Augustauskas, Rytis Kaunas Univ Technol Dept Automat Kaunas Lithuania

ISBN: (数字)9781665462952

ISBN: (纸本)9781665462952

Automated inspection is a principal component in modern manufacturing aiming for Industry 4.0. With the fast development of digital image processing and machine learning algorithms, quality control is often performed by computer vision systems (CV). Following the presented topic, this paper applies the CV system for non-direct length measuring of objects moving through a conveyor. the approach involves image segmentation by a convolutional neural network and segmented object processing principles. Since high-resolution images were used, segmentation was performed in a patch manner. the algorithm also involves a custom contour analysis and the application of the least-squares method for the midline of wooden plank extraction. the proposed approach is robust to the object's non-alignment and merging factors as well as shape and wooden texture variance. the presented algorithm was tested on a custom wooden planks database gathered in a real EURO pallets production environment and confirmed to work as expected ensuring +/- 1mm length measuring accuracy and 0.97s processing timing meeting the real-time requirement of the specified system.

关键词： object segmentation deep learning computer vision digital image processing non-direct measurement

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Optimizing 3D object detection for embedded systems in automated vehicles using sensor data fusion and CUDA computing 18

Optimizing 3D object detection for embedded systems in autom...

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IEEE 18th International conference on Intelligent Computer Communication and processing (ICCP)

作者： Miekkala, Topi Kutila, Matti Schneider, Mathias Hoess, Alfred VTT Techincal Res Ctr Finland Ltd Tampere Finland Tech Univ Appl Sci Amberg Weiden Germany

ISBN: (纸本)9781665464376

this article explores the utilization of the processing power of GPUs using CUDA computation for real-time aggregation of multi-sensor data and detection of 3D objects using parallel clustering algorithms. the purpose is to implement an algorithm that fuses raw lidar point cloud data and 2D camera image object detections to produce 3D object clusters in a lidar point cloud. Most of the computation has been implemented using CUDA parallelism to investigate the capability of GPU devices in this task, which is a common challenge in automated driving. the results indicate that processing times can be optimized within the algorithm, which is crucial when considering the large amounts of data provided by lidar and camera-based systems. the algorithm can perform inference on the Jetson Xavier AGX at rates of similar to 20 to similar to 220 ms depending on the number of objects and their corresponding point amounts in the KITTI dataset.

关键词： 3D object detection CUDA sensor data fusion automated driving

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Post-processing of thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological images

Post-Processing of Thresholding or Deep Learning Methods for...

引用

18th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： image segmentation

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Performance Analysis of a 3D Elliptic Solver on Intel Xeon Computer System 18

Performance Analysis of a 3D Elliptic Solver on Intel Xeon C...

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18th conference on Computer Science and Intelligence systems, FedCSIS 2023

作者： Lirkov, Ivan Paprzycki, Marcin Ganzha, Maria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev bl. 25A Sofia1113 Bulgaria Systems Research Institute Polish Academy of Sciences Ul. Newelska 6 Warsaw01-447 Poland

ISBN: (纸本)9788396744784

It was shown that block-circulant preconditioners, applied to a conjugate gradient method, used to solve structured sparse linear systems, arising from 2D or 3D elliptic problems, have very good numerical properties and a potential for good parallel efficiency. In this contribution, hybrid parallelization based on MPI and OpenMP standards is experimentally investigated. Specifically, the aim of this work is to analyze parallel performance of the implemented algorithms on a supercomputer consisting of Intel Xeon processors and Intel Xeon Phi coprocessors. While obtained results confirm the positive outlook of the proposed approach, important open issues are also identified. © 2023 Polish Information processing Society.

关键词： Conjugate gradient method

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Optical Stealth Algorithm Under Photoelectric Countermeasures and Its Effectiveness Evaluation 18th

Optical Stealth Algorithm Under Photoelectric Countermeasure...

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18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

作者： Yang, Zhonglin Jiang, Yutong Li, Junbao Li, Qiong Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China China North Vehicle Research Institute 4 HuaiShuling Fengtai District Beijing China

ISBN: (纸本)9789819901043

With the diversification of reconnaissance equipment and methods in the military field, optical stealth technology has become a hot spot in military research under the operational needs of optoelectronic confrontation. An improved self-adaptive visible light and infrared waveband stealth algorithm is proposed here, and its effectiveness under photoelectric confrontation is evaluated. the current digital camouflage design method is mainly through background color mixing, image segmentation, spot combination, etc. It has the defects of smooth and clear edge contour, fixed color/texture scene, and cannot adapt to complex texture background or multiple types of scenes. An improved digital camouflage design method based on fuzzy C-means clustering (FCM) image processing technology is proposed to obtain the color characteristics of the background image. then, the color block types of the camouflage spots are acquired to reduce the loss of texture information of the background image caused by clustering or segmentation to avoid the phenomenon of clear boundaries caused by single region segmentation. the results show that the use of adaptive optical stealth algorithms in the visible and infrared bands can reduce the probability of being discovered in optoelectronic countermeasures, thereby gaining a head start. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Towards Bat Bio-inspired Decision-making for Task Allocation in Vehicular Fogs 18

Towards Bat Bio-inspired Decision-making for Task Allocation...

引用

18th Annual International conference on Distributed Computing in Sensor systems (DCOSS)

作者： Quessada, Matheus S. Lieira, Douglas D. De Grande, Robson E. Meneguette, Rodolfo, I Sao Paulo State Univ UNESP Sao Jose Do Rio Preto SP Brazil Fed Inst Sao Paulo IFSP Catanduva SP Brazil Brock Univ BrockU St Catharines ON Canada Univ Sao Paulo Sao Carlos SP Brazil

ISBN: (纸本)9781665495127

Technological evolutions in intelligent transportation have enabled smart and connected vehicles to support novel safety and infotainment services. the provision of such services is guaranteed with effective sharing and allocation of resources for task offloading and processing. the use of vehicular fogs also helps this process by lowering the latency in communications and the resource share among the fog members. However, allocation in Fogs introduces challenges related to the intermittency of Fog vehicle nodes, clustering, topology changes, and resource allocation problems. the use of metaheuristic algorithms has been explored in several works to solve these optimization problems, such as resource allocation, clustering, task allocation, and network communications, especially regarding efficiency. We thus propose a bat bio-inspired decision-making algorithm for task allocation in vehicular fogs called AEGIS. AEGIS uses the cluster members and task parameters to do the decision-making process in the task allocation process that helps to choose the best vehicle of the fog to allocate a determined task. the AEGIS was compared to a GWO approach (meta-heuristic), Greedy, and Random (traditional) approaches. We considered allocated, denied, and lost tasks for the simulation criteria. AEGIS lost fewer tasks than the other algorithms and allocated more tasks than the traditional algorithms.

关键词： Intelligent Transportation systems Vehicular Fog task allocation metaheuristic bio-inspired fogs.

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New Information Extracting and Analysis Methodology for the Terminology Research Purposes: the Field of Biology 18

New Information Extracting and Analysis Methodology for the ...

引用

18th Italian Research conference on Digital Libraries, IRCDL 2022

作者： Jasmonts, Gints Svike, Silga Škirmante, Karina Ventspils University of Applied Sciences 1 Inženieru Street 101 VentspilsLV-3601 Latvia

this study is part of a larger research developing a database of organism names for preserving linguistic diversity and for terminological studies. this article addresses the issue of retrieving the names of organisms from digitized resources of specialized literature for subsequent collection in the new database. the study used image processing algorithms, Optical Character Recognition (OCR) and language recognition algorithms, altogether creating a methodology that reflects the possibility of using data retrieval for the terms of the field of biology in Latvian and other local languages - the names of organisms along with their scientific or Latin name. Further on, these retrieved data are scrutinized and prepared for addition in the developed database. © 2022 Copyright for this paper by its authors.

关键词： Database systems

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NB-IoT based Visual Smart Waste Management System 18

NB-IoT based Visual Smart Waste Management System

引用

18th Baltic Electronics conference

作者： Khan, Sikandar Zulqarnain Hein, Haigo Alam, Muhammad Mahtab Le Moullec, Yannick Parand, Sven Estonian Aviat Acad Dept Aeronaut Engn Tartu Estonia Tallinn Univ Technol Thomas Johann Seebeck Dept Elect Tallinn Estonia Telia Estonia Tallinn Estonia

ISBN: (数字)9781665462952

ISBN: (纸本)9781665462952

NB-IoT technology is characterized by e.g. wide coverage, massive connectivity, reduced device energy consumption, and lower device cost, making it a suitable choice for the Internet of things (IoT) vision. With these NB-IoT provisions, this work presents an NB-IoT based visual Smart Waste Management System (SWMS) that involve the design and development of cost-effective visual smart bins with reduced complexity, low power consumption, and easy installation so as to retrofit the existing traditional bins. thanks to NB-IoT, these smart bins provide long-range connectivity to a multitude of bins so they can transmit their visual data to a remote cloud server. Since the cloud server is a compute-intensive platform, it can further process these images through utilising appropriate image processing (IP), Artificially Intelligent (AI) and Machine Learning (ML) algorithms for efficient waste-management including waste-collection, waste-classification, waste-prediction, and waste-planning.

关键词： Smart Waste Management System Smart bin NB-IoT Edge-computing image processing Cloud computing

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Face Liveness Detection Competition (LivDet-Face) - 2024 18

Face Liveness Detection Competition (LivDet-Face) - 2024

引用

18th IEEE International Joint conference on Biometrics, IJCB 2024

作者： Igene, Lambert Hossain, Afzal Chowdhury, Mohammad Zahir Uddin Rezaie, Humaira Rollins, Ayden Dykes, Jesse Vijaykumar, Rahul Komaty, Alain Marcel, Sébastien Schuckers, Stephanie Tapia, Juan E. Aravena, Carlos Schulz, Daniel Adami, Banafsheh Karimian, Nima Nunes, Diogo Marcos, João Gonçalves, Nuno Sikošek, Lovro Batagelj, Borut Alenin, Aleksandr Alkhaddour, Alhasan Pimenov, Anton Tregubov, Artem Avdonin, Igor Kazantsev, Maxim Pozigun, Mikhail Pryadchenko, Vasiliy Schei, Nima Pabon, David Tiedemann, Manuela Clarkson University United States Idiap Research Institute Switzerland Darmstadt Germany West Virginia University Morgantown United States University of Coimbra Institute of Systems and Robotics Portugal University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia Id R&d Inc Barcelona Spain Hummingbirds Ai FL United States Dermalog Identification Systems GmbH Hamburg Germany

ISBN: (纸本)9798350364132

Imagine a world where a copy of your face could trick the most advanced security systems. this isn't science fiction;it's a real challenge today. LivDet-Face is a competition that aims to advance the detection of attacks at the biometric sensor, known as Presentation Attack Detection (PAD). this international contest is a key benchmark in biometric security, offering an unbiased look at the latest innovations in face PAD and demonstrating progress over time in detecting and preventing sophisticated attacks. through the International Joint conference on Biometrics (IJCB) platform, LivDet-Face 2024 provides a standardized evaluation process, access to advanced Presentation Attack Instruments (PAI), and a comprehensive dataset of bona fide face images. the competition had two main categories: algorithms and systems. A total of sixteen algorithms and one system were submitted for this year's competition. Anonymous submissions topped both image and video subcategories with an ACER of 4.93% and 4.13%, respectively. In the systems category, Team Dermalog, despite being the sole submission, achieved an impressive ACER of 3.12%. © 2024 IEEE.

关键词： Biometrics

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Time-series Anomaly Detection and Classification with Long Short-Term Memory Network on Industrial Manufacturing systems 18

Time-series Anomaly Detection and Classification with Long S...

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18th conference on Computer Science and Intelligence systems, FedCSIS 2023

作者： Markovic, Tijana Dehlaghi-Ghadim, Alireza Leon, Miguel Balador, Ali Punnekkat, Sasikumar School of Innovation Design and Engineering Malardalen University Vasteras72123 Sweden School of Innovation Design and Engineering Malardalen University Vasteras72164 Sweden

ISBN: (纸本)9788396744784

Modern manufacturing systems collect a huge amount of data which gives an opportunity to apply various Machine Learning (ML) techniques. the focus of this paper is on the detection of anomalous behavior in industrial manufacturing systems by considering the temporal nature of the manufacturing process. Long Short-Term Memory (LSTM) networks are applied on a publicly available dataset called Modular Ice-cream factory Dataset on Anomalies in Sensors (MIDAS), which is created using a simulation of a modular manufacturing system for ice cream production. Two different problems are addressed: anomaly detection and anomaly classification. LSTM performance is analysed in terms of accuracy, execution time, and memory consumption and compared with non-time-series ML algorithms including Logistic Regression, Decision Tree, Random Forest, and Multi-Layer Perceptron. the experiments demonstrate the importance of considering the temporal nature of the manufacturing process in detecting anomalous behavior and the superiority in accuracy of LSTM over non-time-series ML algorithms. Additionally, runtime adaptation of the predictions produced by LSTM is proposed to enhance its applicability in a real system. © 2023 Polish Information processing Society.

关键词： Long short-term memory

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