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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是221-230 订阅

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SDN-based Internet of Video things Platform Enabling Real-Time Edge/Cloud Video Analytics

SDN-based Internet of Video Things Platform Enabling Real-Ti...

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International conference on CAD systems in Microelectronics (CADSM)

作者： Orest Kochan Mykola Beshley Halyna Beshley Yuriy Shkoropad Iryna Ivanochko Nadiia Seliuchenko School of Computer Science Hubei University of Technology Wuhan China Department of Information-Measuring Technologies Lviv Polytechnic National University Lviv Ukraine Department of telecommunications Lviv Polytechnic National University Lviv Ukraine Department of Information Systems Faculty of Management Comenius University in Bratislava Bratislava Slovakia Department of Management and International Business Lviv Polytechnic National University Lviv Ukraine Department of Business Economics Investment Lviv Polytechnic National University Lviv Ukraine

the trend of recent years is the continuous development of the Internet of things (IoT). Among such things, a significant share is occupied by visual sensors and video cameras that generate large amounts of data. In turn, the need to attract significant storage resources, transmission throughput, and processing power is an inevitable solution for real-time video analytics. thus, the combination of smart cameras with the computing paradigm of Cloud/Edge and IoT architectures form the next generation of video surveillance systems, called the “Internet of Video things” (IoVT In this paper, a new IoVT platform is developed that, in addition to harmoniously combining Edge/Cloud computing, uses SDN to overcome challenges such as flexible management, control, and maintenance of IoVT devices. In particular, within the proposed IoVT platform, an algorithm for the dynamic selection of Edge or Cloud computing is implemented using an SDN controller to provide effective video analytics in real-time. this algorithm considers such parameters as the priority of computational tasks, the number of video streams, and the image quality with the ability to adapt to a specific application by software configuration of the IoVT platform. We also demonstrate the effectiveness of the proposed solutions on real equipment and discuss several promising areas of application of the developed platform.

关键词： Cloud computing Visual analytics Heuristic algorithms image edge detection Software algorithms Streaming media throughput

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the study of effectiveness of a high-performance crystal lattice parametric identification algorithm based on CUDA technology 5

The study of effectiveness of a high-performance crystal lat...

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5th International conference on Information Technology and Nanotechnology, ITNT 2019

作者： Shirokanev, A.S. Kirsh, D.V. Kupriyanov, A.V. Samara National Research University Moskovskoe Shosse 34A Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia

the study of substances with a crystal structure is a complex multi-step process. the key step in the crystalline substance analysis is the unit cell parameter estimation. the estimation of the crystal lattice unit cell parameters is a particular problem that involves the search of the crystal lattice model's parameters according to the information which can be extracted from the substance. In these recent times, the most accurate information about the substance structure can be obtained with the electron microscope whose linear resolution is high enough to observe the atomic structure of a substance. the problem of parameter estimation in this case means the reconstruction of the three-dimensional crystal lattice with 2-dimentional images received by an electron microscope, and the estimation of the crystal lattice unit cell parameters by reconstructed lattice. In the previous papers the crystal lattice parametric identification algorithms based on solving the local optimization problem were presented. However, the analysis of a large crystal lattice database requires a lot of computations. In this paper, a high-performance crystal lattices parametric identification algorithm using the CUDA technology is proposed. the investigation of the algorithm effectiveness is carried out on the GPU GeForce NVidia GTX 1070 Ti. With data dimension more than 32 translations the acceleration is higher than 70. the algorithm runs more efficiently at the use of a large number of CUDA-blocks. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Parameter estimation

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Parameter assisted HE colored tissue image classification

Parameter assisted HE colored tissue image classification

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IEEE International conference on Intelligent Engineering systems

作者： M. Kozlovszky K. Hegedus S. Szenasi G. Kiszler B. Wichmann I. Bandi Gy. Eigner P. I. Sas L. Kovacs Z. Garaguly V. Jonas G. Kiss G. Valcz B. Molnar Biotech Laboratory Obuda University 2nd Department of Internal Medicine Semmelweis University Molecular Medicine Research Unit Hungarian Academy of Sciences

ISBN: (纸本)9781479908271

the aim of our work was to design and implement a software solution, which supports quantitative histological analysis of hematoxilin eozin (HE) stained colon tissue samples, identify tissue structures - nuclei, glands and epithelium - using image processing methods. Furthermore, based on the result of the histological segmentation, it gives a suggestion for the negative or malignant status of the samples automatically. In this paper we describe the algorithm which builds up mainly by two software components: MorphCheck -our software framework-, which is capable to make effective, morphometric evaluation of high resolution digital tissue images and a modified WND-CHARM (Weighted Neighbor Distance Using Compound Hierarchy of algorithms Representing Morphology), which is a multi-purpose image classifier. the image classification was performed mainly based on 75+15 pre-defined colon tissue specific parameters, which were measured by MorphCheck, and other 2873 in-built generic image parameters, which were measured by WND-CHARM. We appended WND-CHARM's learning and classification capabilities with our colon tissue specific parameters and with this act we have increased its classification accuracy significantly on HE stained colon tissue sample images.

关键词： image classification software solution helium colon tissue images Tissue specimen from colon Epithelium algorithms

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Proximity Contours: Vision based Detection and Tracking of Objects in Manufacturing Plants using Industrial Control systems

Proximity Contours: Vision based Detection and Tracking of O...

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IEEE International conference on Industrial Informatics (INDIN)

作者： S Shishira Vidyadhar Rao Sithu D Sudarsan ABB Corporate Research Center Bengaluru India

In a typical manufacturing setup, field sensors are used to detect the presence of moving material on the production line. Sensory information is interfaced with a centralized control systems for continuous and long distance monitoring. However, the field sensors are prone to failures and may cause frequent down-times which in turn causes reduction in productivity. In this paper, a practical and comprehensive visual inspection system is proposed to automatically control the plant operations. the proposed approach improves productivity and solves several challenges including the speed of execution to support real-time operations, number and choice of image processing stages as well as associated techniques and *** presented solution inspects a production process deployed in metal industry and detects the moving rods on a conveyor platform. the shape of the moving rods is characterized using a combination of GrabCut segmentation, and clustering-based image thresholding methods. To detect and track the movement of rods, proximity contours are identified using low-level pixel information of only two images: one when the conveyor is fully occupied with the rods and the other when the conveyor is empty. Further, this visual sensory information is used to automatically generate control logic for tracking and monitoring a conveyor platform and can be smoothly integrated with industrial control system enabling real-time closed loop operations.

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Segmentation and Classification of Dental Caries in Cone Beam Tomography images Using Machine Learning and image processing

Segmentation and Classification of Dental Caries in Cone Bea...

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17th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Kasputis Zanini, Luiz Guilherme Fischer Rubira-Bullen, Izabel Regina Dos Santos Nunes, Fatima de Lourdes Department of Computer Engineering and Digital Systems University of S˜ao Paulo S˜ao Paulo Brazil Department of Surgery Stomatology Pathology and Radiology University of S˜ao Paulo Bauru Brazil School of Arts Sciences and Humanities University of S˜ao Paulo S˜ao Paulo Brazil

Dental caries are caused by bacterial action that demineralizes tooth enamel and dentin. It is a serious threat to oral health and potentially leads to inflammation and tooth loss if not adequately treated. Cone Beam Computed Tomography (CBCT), a three-dimensional (3D) imaging technique used in dental diagnosis and surgical planning, can potentially contribute to detection of caries. this study aims at developing a computational method to segment and classify caries in CBCT images. the process involves data preparation, segmentation of caries regions, extraction of relevant features, feature selection, and training machine learning algorithms. We evaluated our method performance considering different stages of caries severity based on the International Caries Detection and Assessment System scale. the best results were achieved using a Gaussianfilter with a multimodal threshold with a convex hull for the region of interest segmentation, feature selection via Random Forest, and classification using a model based on k-nearest neighbors algorithm. We achieved outcomes with an accuracy of 86.20%, a F1-score of 86.18%, and a sensitivity of 83.35% in multiclass classification. these results show that our approach contributes to the early segmentation and classification of dental caries, thereby improving oral health outcomes and treatment planning. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Tooth enamel

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Towards the Experience of Daytime Driving at Night (poster) 19

Towards the Experience of Daytime Driving at Night (poster)

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Proceedings of the 17th Annual International conference on Mobile systems, Applications, and Services

作者： Euihyeok Lee Seungwoo Kang KOREATECH Cheonan Republic of Korea

ISBN: (纸本)9781450366618

What if the window of our cars is a magic window, which transforms dark views outside of the window at night into bright ones as we can see in the daytime? In this paper, we investigate the feasibility of implementing such a magic window, addressing two important requirements: (1) the quality of transformed images (from the nighttime to the daytime) and (2) the frame rate of transformed image stream. We present our initial ideas to address the problem and report preliminary results towards the experience of daytime driving at night.

关键词： image interpolation image translation real-time processing

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Development Approaches of Cloud-Based System for Object Recognition on images

Development Approaches of Cloud-Based System for Object Reco...

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International conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Yevhen Bershchanskyi Halyna Klym Department of Specialized Computer Systems Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

Object recognition systems have become integral in various domains, enhancing automation and decision-making processes. this research focuses on developing a cloud-based system specifically designed for efficient object recognition in images. Leveraging the scalability and flexibility of cloud computing, the proposed system not only performs accurate object detection but also ensures secure and scalable image storage. Implemented on the Amazon Web Services (AWS) platform, the system integrates advanced AI algorithms with cloud-native technologies to optimize resource usage and automate workload management. Key contributions include the design of a robust architecture that balances computational loads, enhances recognition accuracy, and streamlines image processing. this study demonstrates the potential of cloud technologies in simplifying object recognition tasks while maintaining security and efficiency.

关键词： Cloud computing image recognition Accuracy Text recognition Face recognition Roads Web servers Object recognition image storage Testing

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FPGA Design of an Automatic Target Generation Process for Hyperspectral image Analysis

FPGA Design of an Automatic Target Generation Process for Hy...

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International conference on Parallel and Distributed systems (ICPADS)

作者： Sergio Bernabe Sebasti´n Lopez Antonio Plaza Roberto Sarmiento Pablo Garcia Rodriguez Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Caceres Spain Institute for Applied Microelectronics (IUMA) University of Las Palmas de Gran Canaria Tafira Baja Spain Grupo de Ingeniería de Medios (GIM) University of Extremadura Caceres Spain

Onboard processing of remotely sensed hyper spectral data is a highly desirable goal in many applications. For this purpose, compact reconfigurable hardware modules such as field programmable gate arrays (FPGAs) are widely used. In this paper, we develop a new implementation of an automatic target generation process (ATGP) for hyper spectral images. Our implementation is based on a design methodology that starts from a high-level description in Matlab (or alternative C/C++) and obtains a register transfer level (RTL) description that can be ported to FPGAs. In order to validate our new implementation, we develop a quantitative and comparative study using two different FPGA architectures: Xilinx Virtex-5 and Altera Stratix-III Altera. Experimental results have been obtained in the context of a real application focused on the detection of mineral components over the Cup rite mining district (Nevada), using hyper spectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experimental results indicate that the proposed implementation can achieve peak frequency designs above 200MHz in the considered FPGAs, in addition to satisfactory results in terms of target detection accuracy and parallel performance. this represents a step forward towards the design of real-time onboard implementations of hyper spectral image analysis algorithms.

关键词： Field programmable gate arrays Hyperspectral imaging Algorithm design and analysis Hardware Vectors image analysis

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Convolutional Neural Networks: Estimating Relations in the Ising Model on Overfitting

Convolutional Neural Networks: Estimating Relations in the I...

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IEEE International conference on Cognitive Informatics

作者： Andrei Gavrilov Alex Jordache Maya Vasdani Jack Deng Bachelor of Applied Science University of British Columbia Vancouver BC Canada Bachelor of Commerce University of British Columbia Vancouver BC Canada Bachelor of Arts University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781538633618

A CNN (Convolutional Neural Network) is one of actively researched and broadly applied deep machine learning methods. A CNN is composed of a feed-forward neural network that takes in images as inputs, and outputs a probability value associated to a class that best describes the image. As well, it is constructed of multiple layers, which include convolutional, max-pooling and fully connected layers. However, the training set has a large influence on the accuracy of a network, and hence it is paramount to create a network architecture that prevents overfitting (when a trained model cannot differentiate newly input data from its test data) and underfitting (the inability of a model to find relationships among inputs). this paper addresses the above deficiencies by comparing the statistics of CNN image recognition algorithms to the Ising model. the Ising model consists of magnetic dipole moments that can be in one of two states: +1 or -1. Using a two-dimensional square-lattice array once a training set of such data is complete, we determine the impact that network parameters, specifically learning rate and regularization rate, have on the adaptability of convolutional neural networks for image classification. Our results not only contribute to a better theoretical understanding of a CNN, but also provide concrete guidance on preventing model overfitting and underfitting when a CNN is applied for image recognition.

关键词： Handheld computers Data models Cognitive systems Convolutional neural networks image processing Cognitive informatics Convolution

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Performance Benchmarking of Drowsiness Detection Methods

Performance Benchmarking of Drowsiness Detection Methods

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IEEE SSD International Multi-conference on systems, Signals and Devices

作者： Jihen Hammedi Imène Ben Ameur Sadok Bazine Abdessalem Ben Abdelalli Electronics and Microelectronics Laboratory University Of Monastir Monastir Tunisia

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Almost half of the car accidents are caused by fatigue and the sleepiness of the driver. the purpose of our work is to propose a non-intrusive and real-time system for drowsiness detection based on facial expression analysis. In this paper, we will find a summary of some research work with a comparative study of performance to determine the most relevant method for detecting drowsiness. We will be particularly interested in analyzing prediction methods by supervising the eyes state of the driver using an image processing technique. the software simulation is performed via a C/C++ code for an I5 processor-based platform, a webcam, and a Linux operating system.

关键词： Webcams Software algorithms Transforms Real-time systems Software Pupils Vehicles

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