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

共 13657 条记录，以下是861-870 订阅

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Underwater image enhancement and detection based on convolutional DCP and YOLOv5 41

Underwater image enhancement and detection based on convolut...

引用

第41届中国控制会议

作者： Liu Guodong Feng Lihui Lu Jihua Yan Lei School of Optics and Photonics Beijing Institute of Technology School of Integrated Circuits and Electronics Beijing Institute of Technology Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Underwater image restoration is conducive to better underwater resource detection and information effective ***,the light in the complex water body diffusely reflective and the selection absorption of different band light results in blurring and color distortion of underwater ***,we propose a convolutional Dark Channel Prior(DCP)underwater image recovery algorithm to enhance underwater *** can do the pre-processing work for the subsequent YOLOv5 object *** enhancement algorithm first performs Commission International Eclairage Lab(CIELAB)equalization of underwater images for color distortion ***,the underwater image formation parameters are estimated by the minimum convolution region ***,Contrast Limited Adaptive Histogram Equalization(CLAHE) is performed to obtain an enhanced underwater ***,the enhanced underwater image is input to the YOLOv5 model for *** results show that the proposed method outperforms state-of-art algorithms in terms of image recovery effect,evaluation quality and detection accuracy.

关键词： image classification color equalization peak signal-to-noise ratio underwater color image quality evaluation

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A Compact Real-Time Thermal Imaging System Based on Heterogeneous System-on-Chip

A Compact Real-Time Thermal Imaging System Based on Heteroge...

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IEEE International conference on Embedded and Real-Time Computing systems and Applications (RTCSA)

作者： Hyun Woo Oh Cheol-Ho Choi Jeong Woo Cha Hyunmin Choi Jung-Ho Shin Joon Hwan Han Infra Technology R&D Center Hanwha Systems Seongnam Republic of Korea

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip (SoC) and uncooled infrared focal plane arrays (IRFPAs). Unlike previous systems that organized separate devices for complex image processing, our system provides integrated image processing support for robust sensor-to-surveillance. The image processing organizes two algorithm stacks: a non-uniformity correction stack to mitigate the distinctive noise vulnerabilities of uncooled IRFPAs, and an image enhancement stack including contrast enhancement and temporal noise filters. We optimized these algorithms for domain-specific factors, including asymmetric multiprocessing (AMP), cache organization, single instruction multiple data (SIMD) instructions, and very long instruction word (VLIW) architectures. The implementation on the TI TDA3x SoC demonstrates that our system can process 640×480, 60 frames per second (FPS) videos at a peak core load of 57.5% while consuming power less than 2.2 W for the entire system, denoting the possibility of processing the 1280×1024, 30 FPS videos from the cutting-edge uncooled IRFPAs. Additionally, our system improves power efficiency by 9.42% and 9.96% at 30 and 60 FPS, respectively, compared to the state-of-the-art when executing similar image processing algorithms.

关键词： Single instruction multiple data Noise Imaging systems architecture Organizations Real-time systems System-on-chip VLIW image enhancement Videos

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4th International conference on Advances in Information Communication Technology and Computing, AICTC 2022

4th International Conference on Advances in Information Comm...

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4th International conference on Advances in Information Communication Technology and Computing, AICTC 2022

ISBN: (纸本)9789811998874

The proceedings contain 51 papers. The special focus in this conference is on Advances in Information Communication Technology and Computing. The topics include: Internet of Things and Retail Performance in an Emerging Market: A Qualitative Analysis;the Impact of Social Media on Consumer Purchase Behaviour;the Contribution of the Internet of Things to Enhance the Brands of Small and Medium-Sized Enterprises in Iraq;trust Chain for Managing Trust in Blockchain-Associated IoT-Enabled Supply-Chains;supply Chain Management Using Blockchain Security Enhancement;a Novel image Encryption Algorithm Using Logistic and Henon Map;an Analysis of Data Sparsity Resolution algorithms Used in Recommender systems;recent Trends for Practicing Machine Learning in Brain Tumors: A Survey;voice-Based Intelligent Virtual Assistant;The Impact of IT Capabilities on Competitive Advantage;Identification of Autism Spectrum Disorder from Functional MRI Using Deep Learning;conversion of Sign Language to Text and Audio Using Deep Learning Techniques;security Attacks and Key Challenges in Blockchain Technology: A Survey;advancing from Manual to Automatic Telecast of News for Deaf;study of Nutrition-Based Recommender System for Diabetes and Cardiovascular Patients Based on Various Machine Learning Techniques: A Systematic Review;comparing Fish Finding Techniques using Satellite and Indigenous Data based on Different Machine Learning algorithms;implementation of E-Governance Framework for Rural Areas of India;opinion Summarization from Online Mobile Phone Reviews Using Feature Based Association Rule Mining;unconventional to Automated Attendance Marking Using image processing;synthetic Animations Based Dictionary for Indian Sign Language;start of Telemedicine in Uzbekistan. Technological Availability;review of Feature Extraction Techniques for Fake News Detection.

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Stereoscopic Distance Filtering Plus Thermal Imaging Glasses Design 44

Stereoscopic Distance Filtering Plus Thermal Imaging Glasses...

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44th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Gibson, Paul L. Hedin, Daniel S. Seifert, Gregory J. Rydberg, Nick Skujing, Janis Boldenow, Patrick Adv Med Elect Maple Grove MN 55369 USA Minnesota HealthSolut Corp Minneapolis MN 55414 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

The authors present the development of eyewear that incorporates stereoscopic and thermal imaging cameras for the purpose of highlighting objects/views of interest. image processing algorithms that simplify complex elements in a scene have the ability to improve the utility of blind and low vision aids. Thermal imaging can be used to highlight important objects such as people or animals, while stereoscopic imaging can be used to filter background imagery beyond a certain distance. The methods used have been successful in providing utility to retinal prosthesis users. The stereoscopic camera systems involved strict requirements on the relative orientation of the cameras for calibrated distance filtering. A mechanical design is presented that fixes the relative camera locations on a 3D printed titanium structure that can float in the frame to maintain orientations even when the eyewear is flexed during wearing.

关键词： Visualization Three-dimensional displays Filtering Stereo image processing image processing Titanium Cameras

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Design and Implementation of Distributed Video AI Super-Resolution Reconstruction System 26

Design and Implementation of Distributed Video AI Super-Reso...

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26th ACIS International Winter conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Shang, Jingtao Guo, Yangliu Zhou, Taolong Li, Wei China North Vehicle Research Institute Beijing China Communication University of China School of Computer and Cyber Sciences Beijing China China Industrial Control Systems Cyber EmergencyResponse China

ISBN: (纸本)9798350345865

This article proposes a distributed video AI super-resolution reconstruction solution based on MapReduce to address the issue of slow video super-resolution reconstruction in a single-machine environment. The solution utilizes scene detection algorithms and precise video segmentation algorithms to divide long videos into multiple video segments based on scenes. These segments are then assigned to different processing nodes for parallel processing, and finally merged to reduce the time required for video AI super-resolution reconstruction. Experimental results demonstrate that this solution effectively improves the speed of video AI super-resolution reconstruction. In addition, this article addresses the issues of low efficiency and low storage space utilization in the distributed storage system HDFS (Hadoop Distributed File System) when storing small files, specifically video segments. It proposes an optimized storage solution based on HDFS and Redis. This solution merges small files and constructs a small file merging and storage model on Redis, effectively alleviating the memory pressure on the NameNode in HDFS when storing a massive number of small files. Experimental results demonstrate that this solution sacrifices a certain degree of read/write performance but mitigates the rapid growth of NameNode memory. This solution holds significant theoretical and practical significance. © 2023 IEEE.

关键词： image segmentation

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Gait Recognition under Different Covariate Conditions using Deep Learning Technique 14

Gait Recognition under Different Covariate Conditions using ...

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14th IEEE International conference on Signal processing and Communications (SPCOM)

作者： Junaid, Iman Ari, Samit Natl Inst Technol Dept Elect & Commun Engn Rourkela 769008 India

ISBN: (数字)9781665482509

ISBN: (纸本)9781665482509

Gait as a biometric has become a popular research topic in recent years as a result of its numerous applications in sectors such as surveillance, authentication, and so on. It is capable of achieving detection at a distance that few other technologies can equal. It is still a difficult problem to solve since real human gait is influenced by several variable elements such as alterations in clothing, speed, and carrying situation. Also, unknown covariate circumstances may impact the training and testing settings for a specific individual in gait recognition. image sequences are typically used by computer-aided gait recognition systems without taking into account variables such as clothes and the contents of carrier bags while on the move. In this work, we provide a technique for selecting gait energy image-based (GEI) features, that is both effective and robust. The covariate factors have less impact on the given gait representation. A simple ten-layered convolutional neural network (CNN) is designed which intakes GEI as input. Several typical variations and occlusions that impact and worsen gait recognition ability are less susceptible to the suggested method. For both clothing and mobility variations, we used the CASIA datasets to assess our observations. The experimental findings reveal that in numerous circumstances, the deep neural network model created in this study achieved better results when compared with other existing algorithms.

关键词： Gait Recognition Covariates Normalization Gait Energy image Convolutional Neural Network

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Thermal image-Based Fault Detection Using Machine Learning and Deep Learning in Industrial Machines: Issues-Challenges and Emerging Trends 4th

Thermal Image-Based Fault Detection Using Machine Learning a...

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4th International conference on image processing and Capsule Networks, ICIPCN 2023

作者： Kulkarni, Vaishnavi V. Hulipalled, Vishwanath R. Kundu, Mayuri Simha, Jay B. Abhi, Shinu School of CSE REVA University Bangalore India School of C&IT REVA University Bangalore India REVA University Bangalore India

ISBN: (纸本)9789819970926

Thermal imaging has long been utilized across industries to maintain electrical equipment and detect faults in machines, ensuring their reliable operation. Infrared thermography, or thermal imaging, has emerged as a powerful, non-contact, non-destructive monitoring tool. Unlike RGB imaging, it offers a non-intrusive approach that respects privacy and does not rely on ambient lighting. As heat signatures vary based on defects or machine conditions, infrared imaging is essential for effective condition monitoring. This survey study introduces the principles and applications of thermal imaging in fault detection and a comprehensive analysis of recent developments and approaches in the field. The survey encompasses various machine types, including electrical systems, mechanical components, and industrial equipment. Multiple techniques, algorithms, and deep learning models for image processing, feature extraction, and fault classification are extensively discussed. The paper also includes an in-depth literature review on fault detection in distinct machines, such as induction motors, rotating machines, and transformers. The survey aims to identify open challenges and issues in fault detection for different machines, such as induction motors and transformers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fault detection

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Developing an Efficient image Encryption Algorithm for Secure Data Transmission in Healthcare systems 2

Developing an Efficient Image Encryption Algorithm for Secur...

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2nd IEEE International conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Ali, Hassan Mohamed Reddy, P Niranjan Rao, S. Govinda Dixit, Krishna Kant Santhi, G.B. Kurukuntla, Srinivasa Reddy The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Telangana Hyderabad India GRIET Bachupally Department of CSDS Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India Teegala Krishna Reddy Engineering College Department of ECE Hyderabad India

ISBN: (纸本)9798350368109

As the use of Healthcare Information Technology has continued to rise, the issue of safe transfer of medical information with especial emphasis on images has become a major concern. The findings of this study propose a new approach to the encryption of images that is well suited to applications in healthcare and which guarantees the safety of medical images over weak networks. This proposed algorithm successfully implements a chaotic encryption method alongside wavelet transformation so as to design an effective and virtually invulnerable encryption method that does not consume an excessively large amount of computing power. There is also a way for the algorithm to be tested on X-Ray, CT, and MRI images so that the algorithm is not confined to one particular imaging modality. Such results clearly prove that the proposed algorithm provide better performance than AES as well as RSA with respect to encryption speed and sensitivity of the keys. image quality after decryption is also maintained making it possible to retain important information about the patient's health. Due to the algorithms high security, efficiency and capability of processing data in real time healthcare environment the proposed algorithm, provides a feasible solution for securing sensitive healthcare information and is a worthy addition to the literature on healthcare data security. © 2024 IEEE.

关键词： Electronic health record

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction 5

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

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5th International conference on Computer Vision, image and Deep Learning, CVIDL 2024

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China South China University of Technology School of Electronic and Information Engineering Guangzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350373820

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. © 2024 IEEE.

关键词： Trajectories

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Ingenious Waste Management: Implementing Machine Learning in Waste Segregation 6

Ingenious Waste Management: Implementing Machine Learning in...

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6th IEEE International conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Asha, V. Nithya, B. Govindaraj, M. Brunda, T.M. Hiremath, Ashwini Jayaprakash, B. New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306613

Waste management is a pressing global issue, and the need for efficient waste separation processes is becoming increasingly important. Incorporating Machine Learning techniques with waste separation has yielded promising results and revolutionized the waste separation process. However, this research paper highlights the gaps that exist in the understanding and efficacy of machine learning for waste separation. This study provides an overview of state-of-the-art techniques for waste separation using machine learning, including the use of computer vision, sensor technology, and neural networks. This paper portrays case studies with empirical data from various waste management establishments to demonstrate the potential advantages of machine learning for recycling and waste classifying. Although progress has been made, this article highlights some significant gaps in our knowledge. These gaps include the need for standardized datasets, optimization of algorithms for real-world scenarios, the impact of technology on the environment, and the socio-economic aspects of integrating automation into waste management. The essential and most important tasks of intelligent mechanisms in the digital age is waste separation. Segregation of waste is necessary to apply proper disposal and waste management strategies. The existing systems use drones to identify waste by using deep learning, image processing, GPS, and GSM approaches, locating and notifying the authorities about them. The improvement made is in the analysis and utilize multi-object detection and picture classification to accomplish trash segregation. Waste management may be effectively accomplished with higher accuracy through Machine Learning practices which therefore considerably lowers labor expenses. © 2024 IEEE.

关键词： image Detection Machine Learning Prediction Sensor Waste Segregation

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