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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13662 条记录，以下是1241-1250 订阅

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Improving Sparse Dictionary Learning with Rejection of Fully Exposed Patches

Improving Sparse Dictionary Learning with Rejection of Fully...

引用

European Signal processing conference (EUSIPCO)

作者： Matthieu Chancel Hacheme Ayasso Michel Desvignes Jean-Michel Vignolle Eric Lespessailles GIPSA-Lab Univ. Grenoble Alpes CNRS Grenoble INP Grenoble France Image Group TRIXELL Moirans France Department of Rheumatology Centre Hospitalier Universitaire Régional d'Orléans Orléans France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Sparse dictionary learning is widely used in image processing tasks such as denoising, classification and image enhancement. However, learning features from X-ray images can be challenging since some parts of the image contains no or very little clinical information which makes them totally exposed to X-rays. These areas are irrelevant for medical content reconstruction and waste the space in the dictionary. Moreover, they slow down the convergence of the learning methods. In this paper, we present a prior knowledge about these fully exposed areas based on their physical distribution and propose a specialized dictionary learning algorithm to improve convergence speed and the quality of the learned dictionary while keeping the same quality of reconstruction. We compare our solution to a variance based approach and show that our solution is robust to the X-ray dose and then more efficient than the variance-based approach.

关键词： Learning systems Dictionaries Noise reduction Signal processing algorithms Machine learning Signal processing X-ray imaging image reconstruction image enhancement Convergence

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Design of a College Student Mental Health Assessment System Based on Improved Apriori Algorithm

Design of a College Student Mental Health Assessment System ...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Lanfang Mao College of Surveying Mapping and Geographic Information Lanzhou University of Resources and Environment Lanzhou Gansu China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper emphasizes the need to design an enhanced Apriori algorithm-based mental health assessment system to address the mental health issues college students face. The article aims to assess the current mental health evaluation systems of college students and develop a framework for a mental health evaluation. It proposes using data mining algorithms, precisely the Apriori algorithm, to analyze and categorize the data. This process ultimately yields intelligent mental health evaluation results. Finally, the mental health astute assessment system's practicality and superiority are examined by targeted simulation tests. The findings demonstrate that the system described in the article effectively addresses the limitations of the existing mental health intelligence evaluation system. It enhances the precision and efficiency of mental health intelligence evaluation while ensuring system stability. Consequently, it satisfactorily fulfills college students' current mental health evaluation needs.

关键词： Databases image processing Mental health Medical services Computer applications Stability analysis Data systems Data mining Faces

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Research on Intelligent Data processing Support Model of Government Information System Based on Deep Learning

Research on Intelligent Data Processing Support Model of Gov...

引用

Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe conference on

作者： Dapeng Lu Haojie Liu School of Tourism and Public Administration Zhuhai College of Science and Technology Guangdong Zhuhai China Computer Application Technology Henan University of Technology Zhengzhou China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This study constructs a data processing model that supports government information system. By designing a deep learning algorithm suitable for government scenarios, intelligent processing functions such as text analysis and image recognition are integrated into the system, aiming to achieve accurate processing of multi-source government data. The model design includes data preprocessing, feature extraction, algorithm optimization and adaptive learning modules to improve the recognition and analysis capabilities of complex government data. Through system simulation, this paper verifies the effectiveness of the proposed model. The simulation data shows that the model is significantly superior to traditional methods in data processing speed, accuracy and resource utilization. Precise experimental results show that the accuracy of the model in text data classification, image data recognition and other aspects reaches more than 96%. At the same time, it can effectively shorten the processing time in large-scale data processing tasks, providing technical support for the practical application of intelligent government information system. This study provides an efficient and scalable solution for intelligent data processing in government information system.

关键词： Deep learning Adaptation models Accuracy image recognition Heuristic algorithms Government Data processing Data models Optimization Information systems

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Skin temperature classification by image processing 16

Skin temperature classification by Image Processing

引用

16th Iberian conference on Information systems and Technologies (CISTI)

作者： Caridade, Cristina M. R. Roseiro, Luis ISEC Polytech Coimbra Coimbra Portugal ISEC Appl Biomech Lab Coimbra Portugal CICGE Ctr Invest Ciencias Geoespaciais Vila Nova De Gaia Portugal

ISBN: (纸本)9789895465910

Currently, body map temperature is one of the most commonly used indicators of human health status. The use of portable thermal camera equipment for temperature measurements in the human skin is nowadays a reality. This work aims to present an automatic method to skin temperature classification using image processing, investigating the potential role of temperature changes in skin inflammation. Infrared thermography (IRT) was used to assess the thermogenic response of the hand skin. Characteristics of the map temperature distribution in some anatomical regions of the hand were collected. The tests were realized to demonstrate the potential of using image processing to identify and define specific regions of temperatures and their characteristics. The thermal information provided by these imaging systems can be used for monitoring purposes, as some previous research demonstrated. However, to develop more accurate algorithms, larger training and testing sets must be used.

关键词： Thermographic image image processing Skin inammation Monitoring temperature map

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Autofocus optical imaging system based on image processing 14

Autofocus optical imaging system based on image processing

引用

14th International conference on Information Optics and Photonics, CIOP 2023

作者： Teng, Teng Chen, Guan Xiaofei, Wang Hua, Fan Dongjie, Ju Zhaojuan, Chi Jinan250000 China Shandong JINGWEI Intelligent Equipment Co. Ltd Weifang261000 China

ISBN: (纸本)9781510671744

Autofocus optical imaging systems are widely used in industrial inspection, medical diagnosis, drone photography, machine vision and other fields. image-based autofocus algorithms typically consist of two key steps: image sharpness evaluation and search strategy. In this study, we propose an advanced image-based autofocus algorithm specifically designed for industrial image measurement. In order to improve the accuracy of the clarity assessment, we improve the existing method by introducing Gaussian filtering and threshold controlled Laplace operators. This improvement effectively reduces the effects of noise and light intensity changes, improving the reliability of clarity measurements. In addition, we propose a novel distance adjustment strategy combining coarse and fine tuning as part of the search strategy. This strategy reduces the interference of local peaks, allowing the algorithm to accurately identify the best image focus. The proposed image-based autofocus algorithm has several advantages, e.g. high focusing accuracy, high repeatability, and stability under light intensity changes2. These advantages make it ideal for industrial image measurement applications. To verify the effectiveness of our method, we build an experiment setup in a lab using ADLINK PCI-9114-DG board and a custom-designed lens. The results show that the sensitivity and performance of the autofocus system meet certain requirements. In summary, the image-based autofocus algorithm that we developed enables accurate and reliable focusing. It overcomes some challenges such as noise and light intensity changes to ensures optimal image focus, and improves overall image quality. The successful implementation of our autofocus system can benefit industrial inspection, medical diagnostics and many other applications. © 2023 SPIE.

关键词： Diagnosis

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Innovative System of Cameras Allowing to Coordinate the Flight of a Swarm of UAVs

Innovative System of Cameras Allowing to Coordinate the Flig...

引用

International conference on Control and Robots (ICCR)

作者： Jens Müller Joachim Horn Kevin Larkin Marie Schweim Anne Schweim Dept. of Control Engineering Helmut-Schmidt-University Hamburg Germany Dept. of Materials Science and Engineering Carnegie Mellon University Pittsburgh (PA) USA

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

This paper aims to deliver insights into an innovative camera system called the Swarm Flight Unit (SFU). The SFU enables unmanned aerial vehicles (UAVs) to coordinate their movements as a swarm without any external help. Over the course of several years of development, conventional image processing algorithms were used to tackle the task of identifying and localizing drones in real time. This paper will describe applied methods, show the results of validation tests performed within a dedicated test bench, and prospect further actions regarding device integration.

关键词： Performance evaluation Robot kinematics image processing Robot vision systems Cameras Autonomous aerial vehicles Real-time systems Drones

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Adapting Spatial Transformer Networks Across Diverse Hardware Platforms: A Comprehensive Implementation Study

Adapting Spatial Transformer Networks Across Diverse Hardwar...

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IEEE International conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Meriem Bettayeb Eman Hassan Muhammad Umair Khan Yasmin Halawani Hani Saleh Baker Mohammad Computer and Communication Engineering Department System-on-Chip (SoC) Lab Khalifa University Abu Dhabi UAE College of Engineering and IT University of Dubai Dubai UAE

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

The field of artificial intelligence (AI) holds a variety of algorithms designed with the goal of achieving high accuracy at low computational cost and latency. One popular algorithm is the vision transformer (ViT), which excels at various computer vision tasks for its ability to capture long-range dependencies effectively. This paper analyzes a computing paradigm, namely, spatial transformer networks (STN), in terms of accuracy and hardware complexity for image classification tasks. The paper reveals that for 2D applications, such as image recognition and classification, STN is a great backbone for AI algorithms for its efficiency and fast inference time. This framework offers a promising solution for efficient and accurate AI for resource-constrained Internet of Things (IoT) and edge devices. The comparative analysis of STN implementations on the central processing unit (CPU), Raspberry Pi (RPi), and Resistive Random Access Memory (RRAM) architectures reveals nuanced performance variations, providing valuable insights into their respective computational efficiency and energy utilization.

关键词： Accuracy Resistive RAM Transformers Hardware Computational efficiency Classification algorithms Central processing Unit

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FPGA Implementation of QC-LDPC Decoder with Rapid Cyclic Shifter for CV-QKD systems

FPGA Implementation of QC-LDPC Decoder with Rapid Cyclic Shi...

引用

Communication, image and Signal processing (CCISP), International conference on

作者： Zeyuan Jin Yinghua Xie Xue-Qin Jiang Genlong Chen School of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

In continuous variable quantum key distribution (CV-QKD) systems, low-density parity check (LDPC) decoders have gained widespread adoption due to their robust error correction capabilities and good adaptability to parallel processing hardware designs. We introduce a syndrome-based normalized min-sum algorithm (S-NMSA) and implement the decoder on FPGAs for high throughput and minimal resource consumption. The decoder utilizes a semi-parallel architecture. The proposed rapid cyclic shifter (RCS) can complete the shift of any dimension matrix in one clock cycle and realizes a significant reduction in hardware resource consumption. The semi-parallel decoding structure attains a 68.5 Mbps processing throughput on the condition that the system clock of the quasi-cyclic (QC) LDPC decoder is 250 Mhz.

关键词： Phase change materials Signal processing algorithms Throughput Parity check codes Hardware Decoding Quantum key distribution Resource management Field programmable gate arrays Clocks

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Application of Time Series Anomaly Detection algorithms in Risk Identification

Application of Time Series Anomaly Detection Algorithms in R...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International conference on

作者： Dawei Li Jian Zhang Shuai Yang Jian Zhao Bin Zhou Lei Xu Competition Policy and Assessment Center of State Administration for Market Regulation Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Traditional risk identification methods often rely on manual inspections and post-event processing, which struggle to cope with the vast amounts of data and the real-time changes in the internet environment. Time series anomaly detection algorithms, as an effective data analysis tool, can identify abnormal changes in time series data. By utilizing spectral residual anomaly detection algorithms and sub-sequence anomaly detection algorithms, models were constructed to conduct an in-depth anomaly detection analysis of price data on the JD platform, providing a new solution for risk identification. The experimental results show that the precision, recall, and F1 scores of these two time series anomaly detection algorithms are significantly better than those of other comparative algorithms, demonstrating significant application value in risk identification.

关键词： Sensitivity image processing Time series analysis Manuals Inspection Real-time systems Object recognition Detection algorithms Anomaly detection Monitoring

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Post refocusing algorithm for high resolution spaceborne SAR image processing in large rolling terrain

Post refocusing algorithm for high resolution spaceborne SAR...

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Yang Gao Tianshun Xiang Juanjuan Yang Hongxing Dang Xiaomin Tan China Academy of Space Technology Xi’an China

In spaceborne SAR image processing, focusing algorithms are applied with a uniform altitude throughout the observation area. The uniform altitude assumption shows good performance in most spaceborne SAR systems. However, in the high resolution (<0.5m) SAR systems, the defocusing becomes significant in areas with high topographic variations. This letter proposes a new post refocusing algorithm capable of improving the focusing quality of the image with large rolling terrain. Simulate data of spaceborne SAR system are used to demonstrate the performance of the proposed approach.

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