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

共 15616 条记录，以下是601-610 订阅

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Research on image processing and Recognition algorithms in Software Information systems Based on Deep Learning

Research on Image Processing and Recognition Algorithms in S...

引用

Electrical Engineering, Big Data and algorithms (EEBDA), IEEE International conference on

作者： Xiaojun Tang Radio and Television Monitoring Center of Shandong Province Jinan Shandong China

With the rapid development of deep learning technology, its application in image processing and recognition has become a hot research topic. The application of these technologies in software information systems such as automatic image classification, facial recognition, and medical image analysis not only improves processing efficiency but also enhances recognition accuracy. This article focuses on the training and deployment of adversarial generative network models in deep learning algorithms for graphic processing and recognition applications. Finally, two sets of simulation experiments were conducted, and the results are as follows: in the face recognition system, the recognition accuracy optimized by the deep learning algorithm based on adversarial generative networks improved by an average of 8.9% compared to traditional methods. In the traffic sign recognition system, the overall average recall index improved by 12.45%. This article proposes an improved network architecture and training strategy, which significantly improves the quality of image generation and the accuracy of recognition models in software information systems.

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CAT-DG: A Cross-Attention-Based Domain Generalization Model for Medical image Segmentation 20th

CAT-DG: A Cross-Attention-Based Domain Generalization Model ...

引用

20th International conference on Intelligent Computing (ICIC)

作者： Gao, Wenhui Shi, Yilun Yu, Lei Xu, Qiaozhi Inner Mongolia Normal Univ Coll Comp Sci & Technol Hohhot 010022 Peoples R China Inner Mongolia Autonomous Reg Peoples Hosp Hohhot 010020 Peoples R China

ISBN: (纸本)9789819755967;9789819755974

In medical image segmentation tasks, the performance of the trained segmentation model in the unseen domain is affected by the domain shifting problem. Therefore, improving the model's generalization is crucial for the practical application of intelligent models for medical images. The domain generalization model for medical image segmentation is typically divided into two stages: data augmentation and segmentation training, and most existing studies focus on improving the data enhancement stage, little consideration is given to the training stage. However, it is very important to consider the model's generalization in the segmentation training stage to alleviate the domain shifting problem. To validate the idea, we propose the CAT-DG, a cross-attention-based domain generalization model for medical image segmentation. The model pays more attention to the image content information and ignores the style information during segmentation training phase, resulting in an average improvement of 1.7%-7% compared to other methods in unseen domains. Additionally, we propose a hybrid loss function that combines Dice loss and Focal loss, and the loss calculation incorporates the distillation idea tomitigate the impact of class imbalance inmedical images on model performance, which improves accuracy by 3% compared to not using the hybrid loss. The detailed experiment results not only prove the effectiveness of the CAT-DG but also demonstrate that considering the model generalizability during segmentation training phase can furtherly alleviate domain shifting problems.

关键词： Domain Generalization Cross-attention Medical image Segmentation

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Fake Face image Classification by Blending the Scalable Convolution Network and Hierarchical vision Transformer 1

引用

4th International conference on Computer and Communication Technologies, IC3T 2022

作者： Kerenalli, Sudarshana Yendapalli, Vamsidhar Mylarareddy, C. Department of CSE SoT GITAM University Karnataka Bengaluru562163 India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

A face has been used as a primary and unique attribute to authenticate individual users in emerging security approaches. Cybercriminals use the double-edged sword "image processing" capabilities to deceive innocent users. The underlying technology is based on advanced machine learning and deep learning algorithms. The intentions of cyber criminals range from simple mimicking or trolling to creating violent situations in society. Hence, it is necessary to resolve such problems by identifying the fake face images generated by expert humans or artificial intelligent algorithms. Machine learning and artificial neural networks are used to resolve the issue. In this work, we have designed an approach for detecting deep learning-generated fake face images by combining the capabilities of the scalable convolutional neural networks (CNN) "EfficientNet" and hierarchical vision transformer (viT) "shifted window transformer". The proposed method accurately classifies the fake face images with a 98.04% accuracy and a validation loss of 0.1656 on the 140 k_real_fake_faces image dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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A Survey on Crime Detection using CCTV systems 3

A Survey on Crime Detection using CCTV Systems

引用

3rd International conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sheela, A Jeba Balaji, S. Balaji, B. Hemanth Kumar, U. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

In order to improve security measures across numerous domains, such as public safety, transportation, and critical infrastructure protection, the use of closed-circuit television (CCTV) systems for threat analysis has become increasingly important. The technical abstract gives a summary of the most recent algorithms and techniques used in threat analysis when using CCTV. This study emphasizes the importance of spatial and temporal reasoning, machine learning models like Support Vector Machines (SVM), Haar-Cascade, viF (violent Flows), OviF (Oriented violent Flows), RINOC (Real-time image and video Object Classification), and LRT (Live-Streaming Real-Time). Insights into the diverse methodologies that can be applied to enhance the effectiveness of CCTV-based threat analysis, the efficiency of CCTV systems and their impact on detecting and reducing criminal activities are discussed. The comparative outcomes of different detection approaches and their ensembles have been empirically discussed. © 2023 IEEE.

关键词： CCTV (Closed Circuit Television) CNN Deep learning Haar-Cascades image Classification image processing SVM (Support Vector Machine)

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vision and control capabilities of autonomous systems with the Kria Adaptive System-on-Modules 31

Vision and control capabilities of autonomous systems with t...

引用

2023 31st National conference with International Participation, TELECOM 2023

作者： Gorbounov, Yassen Peychinov, Petar University of Mining and Geology "St. Ivan Rilsky" Department of Automation of Production Systems Sofia Bulgaria New Bulgarian University Department of Informatics Sofia Bulgaria

ISBN: (纸本)9798350303292

The role of computer vision in autonomous control systems is increasingly growing. Along with this, there is a steady trend of increasing requirements for sensors performance, the speed of image processing algorithms, and the complexity of their implementation with the means of digital computing. This research explores the prospects for vision and control in a potentially dynamically changing environment using the recent system-on-module (SoM) Kria manufactured by AMD/Xilinx. Acceleration based on heterogeneous systems incorporating user reconfigurable logic is considered a promising approach to improve performance and energy efficiency due to the high level of parallelism they offer. Therefore, the structure of the Kria SoM platform and the processing pipeline are examined in this paper. Object recognition experiments have been conducted which aim to demonstrate the suitability and accessibility of adaptive computing as a good candidate that can respond to the changing demands of a wide range of robotic applications. © 2023 IEEE.

关键词： Energy efficiency

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Robotic Manipulator Software and Hardware Development Using Recognition algorithms

Robotic Manipulator Software and Hardware Development Using ...

引用

2024 conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024

作者： Borovkov, Grigoriy S. Martirosyan, Karina V. Protasov, Yakov A. Saint Petersburg Mining University Department of 'System Analysis and Control' Saint Petersburg Russia North-Caucasus Federal University Branch in Pyatigorsk Control Systems and Information Technologies Department Pyatigorsk Russia

ISBN: (纸本)9798350360646

The enterprises digitalization determines the development trends of the industrial sector. There is a need to reduce the percent of human participation in processes associated with conveyor production, which requires the high-tech solutions integration. Due to the lack of quality control on the assembly line, enterprises produce a large percentage of defective products. The article discusses a possible solution to the problem of image processing for monitoring defects in products moving along a conveyor belt. The use of neural network technologies allows us to identify defects in production in real time, and the use of a robotic arm allows us to immediately remove such products from the assembly line. As a result of the research, it is planned to implement a software and hardware complex of a robotic manipulator using computer vision technologies that can distinguish objects and mechanically influence them. © 2024 IEEE.

关键词： Arduino automated sorting system computer vision data collection and analysis defect detection image classification neural networks product quality control robotics in manufacturing statistics

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Fish Detection and Quantity Estimation Based on Sonar images 1st

Fish Detection and Quantity Estimation Based on Sonar Images

引用

1st International conference on Extended Reality, ICXR 2024

作者： Du, Wenxiang Yan, Shuai Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Qingdao Research Institute of Beihang University Qingdao266100 China

ISBN: (纸本)9789819636785

Due to the complex underwater environment and dense targets, it is difficult to quickly and accurately calculate the location and quantity of fish targets in marine ranching. We have developed an underwater target detection and counting algorithm based on sonar images, which includes four parts: image preprocessing, background subtraction, contour detection, and target counting. Firstly, we design a sliding-window-based gain algorithm based on the uneven grayscale value of sonar images, which amplifies the effective signal while smoothing the grayscale of the image. Secondly, background subtraction is used to separate the foreground and background of the sonar image, which can remove background noise and filter the target using a filtering algorithm to generate a binary image with clear targets. Then, target detection is combined with morphological processing techniques and contour detection algorithms. Finally, we use image erosion technology to separate overlapping targets, calculate the contour position of the targets, and count the quantity. A large number of results indicate that our algorithm can quickly and accurately detect the position of fish in sonar images while also counting the number of fishes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binary images

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Design of a Facial Recognition System for Power Grid video Surveillance Based on Deep Learning algorithms

Design of a Facial Recognition System for Power Grid Video S...

引用

2024 International conference on Power, Electrical Engineering, Electronics and Control, PEEEC 2024

作者： Liu, Yuan Miao, Chunyuan Mei, Lin Lv, Jin Jiang, Geli Henan Zhengzhou450000 China Nanjing Nari Information and Communication Technology Co. Ltd Jiangsu Nanjing211100 China

ISBN: (纸本)9798350378917

In recent years, with the extensive application of deep learning methods, face recognition technology has been greatly developed. Aiming at the problem of video surveillance in power network, a video surveillance method based on image processing is proposed. In this paper, an intelligent decision model based on knowledge automatic extraction is studied and optimized. In power network monitoring, face detection and recognition technology based on deep learning is a commonly used face detection and recognition technology, including: face detection, face alignment, face size alignment. Aiming at video surveillance image in power system, a new technology with strong robustness is studied. This new algorithm can solve this problem well. On this basis, a power network monitoring system based on computer vision technology is adopted. Experimental results show that the proposed method can achieve {9 5. 9%} accuracy and {9 4. 7%} stability, and can be used in real environment. © 2024 IEEE.

关键词： Power systems computer aided design

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A Comparison Between CPU and GPU Computing in DNN-Based DoA Estimation

A Comparison Between CPU and GPU Computing in DNN-Based DoA ...

引用

UNIfied conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Kokkinis, Georgios Ahmed, Qasim Z. Sambell, Alistair Chochliouros, Ioannis P. Lazaridis, Pavlos I. Zaharis, Zaharias D. School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom Marousi151 24 Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783031494208

Deep Learning algorithms have recently become accessible to modern Telecommunication systems due to the advancement in both hardware and software technology. The massively parallel computational tasks of Artificial Intelligence (AI) training are now directly performed in Graphic processing Units (GPUs), which contain many processing cores. This paper tests the improvement in computing time for Deep Neural Network (DNN) architectures that solve the Direction of Arrival (DoA) estimation problem as a classification task by utilizing the Nvidia CUDA framework. The training and testing data are generated by receiving signal information from a simulated antenna array. The input of the DNN is a 64 × 64 image with two channels, whereas the problem is modelled as a multi-label classification task. The purpose of this research is to observe the acceleration that the GPU provides in the DoA estimation classification problem. The project includes benchmarks from a wide range of computing systems and the results demonstrate the potential of DNN-based DoA estimators as a modern technology that can be deployed on available commercial hardware. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Graphics processing unit

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Informational image of a Person's Gait According to Mobile Phone Data

Informational Image of a Person's Gait According to Mobile P...

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2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Dorofeev, Nikolay Grecheneva, Anastasya Sharapov, Ruslan Vladimit State University Murom Institute Vladimir Russia

ISBN: (纸本)9781665464291

The article describes the results of the research of an information digital model (map) of a person's gait based on preliminary processing of mobile phone accelerometer signals in biometric authentication systems, as well as a personal health monitoring systems. The relevance of collecting biometric information extracted is explained from human gait data. The features of the primary processing of data are briefly described from the accelerometer of a mobile phone in order to extract gait parameters. The results of the research of the influence of various factors are described on the data of a mobile phone accelerometer. The obtained results of the research indicate the possibility of separating gait data into different groups and further monitoring the change in parameters within each group. Building a personal information model (map) of a person's gait will make it possible with a preliminary division into data categories. It is to more effectively apply various processing algorithms and improve the accuracy of assessing individual gait characteristics and the quality of the entire system as a whole. © 2023 IEEE.

关键词： Accelerometers

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