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

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image Analysis System for Augmented Reality Games 13th

Image Analysis System for Augmented Reality Games

引用

13th International conference on image processing and Communications, IP and C 2023, 13th International conference on Computer Recognition systems, CORES 2023

作者： Garbat, Piotr Galiński, Grzegorz Żakowski, Michal Bieniek, Mateusz Lasocki, Maciej Institiute of Microelectronics and Optoelectronics Warsaw University of Technology Nowowiejska 15/19 St. Warsaw Poland Institiute of Radioelectronics and Multimedia Technology Warsaw University of Technology Nowowiejska 15/19 St. Warsaw Poland Faculty of Architecture Warsaw University of Technology Koszykowa 55 St. Warsaw Poland

ISBN: (纸本)9783031416293

This article presents the idea of a vision system allowing the analysis of images from multiple cameras for the needs of augmented reality systems. The presented solution allows for the effective determination of the positions and movement paths of people moving in urban space and their activities. The article presents the key modules of multi-view image analysis, including the gamer detection and tracking (MOT) subsystem and the user activity detection subsystem. The discussed subsystems have been tested in order to determine the effectiveness of the proposed algorithms for determining the movement path and recognizing activity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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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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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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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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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...

引用

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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Application of an Advanced Dijkstra Algorithm for Automation systems of 3D Piping Routing 8

Application of an Advanced Dijkstra Algorithm for Automation...

引用

8th International conference on Communication and Information systems, ICCIS 2024

作者： Choi, So-Won Lee, Eul-Bum Pohang University of Science and Technology Graduate Institute of Ferrous and Eco Materials Technology Pohang Korea Republic of

ISBN: (纸本)9798331531171

As engineering, procurement, and construction (EPC) plant projects increase in size and complexity, the importance of accurate design in a short time frame is rising. In particular, the automation of piping route design is essential to shorten the project schedule and reduce the cost. This study aims to develop an automated pipeline route design model and proposes an advanced Dijkstra algorithm. First, digital image processing technology was applied to preprocess the data. In addition, 3D model data of plant equipment was set up to realize the developed model in 3D. The Advanced Dijkstra algorithm proposed in this study for piping design automation consists of three algorithms. First, the Dijkstra algorithm was applied to find the shortest route, and the Routing Priority algorithm, which considers the importance of design, was added to optimize the route. In addition, the Interference Obstacle Avoidance algorithm was used to consider errors caused by interference between equipment and pipes. The automatic piping route design model developed in this study was verified using the project data of the 'D' petrochemical plant in Korea. As a result of comparing the Bill of Material (BoM) extracted through the developed model with the BoM of the actual completed project, the matching accuracy was 98.9% in weight and 84.9% in length for the pipe quantity. The automatic pipe route design model is expected to store project data to manage advanced design know-how and contribute to sustainable growth in the field of pipe design. © 2024 IEEE.

关键词： Advanced Dijkstra algorithm digital image processing EPC Projects piping auto-routing model

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Dog Breed Detection using YOLOv7 and WOA-CNN 10

Dog Breed Detection using YOLOv7 and WOA-CNN

引用

10th International conference on Communication and Signal processing, ICCSP 2024

作者： Singh, Aniket Kothari, Rushil Arulalan, V. Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350353068

The need to accurately identify dog breeds is important due to their popularity and role as companions. With an increase in the variety of dog breeds worldwide, there is a pressing demand for better classification methods. Advances in artificial intelligence have resulted in the creation of a new model that combines YOLOv7 with the Whale Optimization Algorithm (WOA) in a Convolutional Neural Network (CNN) setup, designed specifically for detecting dog breeds. This model uses the extensive Stanford Dogs dataset, which includes 120 unique breeds, and has been thoroughly tested. It has shown to be more accurate and precise than current methods. The effectiveness of this model in identifying dog breeds marks a significant improvement in the field and suggests its usefulness in various areas such as veterinary services, dog behavior studies, and improved pet management systems. Additionally, this method showcases the benefits of merging advanced machine learning techniques with nature-inspired algorithms to address complex identification challenges, making a significant contribution to the development of AI-based solutions in animal identification and related areas. © 2024 IEEE.

关键词： image recognition

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AI-Based Bone Cancer Detection Using image processing and CNN 5th

AI-Based Bone Cancer Detection Using Image Processing and CN...

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5th EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： Srividya, K. Reddy, Gangannagari Varunteja Bakki, Vishwaja Adilakshmi, T. Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9783031770777

This study presents a new Technology-driven approach for early detection of bone cancer using preliminary image processing technologies and neural networks (CNN) used for diagnosing cancer from pathological images. The main aim is to provide a powerful and reliable tool for cancer diagnosis done in clinics. Survival rates of cancer decrease with an increase in age, urging the need for early diagnosis and precaution. The project aims to use machine learning and image preprocessing to provide solutions for the detection of cancer, necessary for timely intervention. Using carefully selected datasets and advanced image preprocessing techniques, the project aims to provide doctors with reliable tools to accurately diagnose bone cancer. Analysis of existing literature demonstrates the effectiveness of CNN and deep learning algorithms in the analysis of medical images as the basis of the proposed method. The proposed methodology includes image enhancement, data acquisition, CNN development, training, validation, transformation, and model evaluation. The CNN model is evaluated on accuracy, precision, recall, and f1-scores of bone cancer diagnosis images. It provides insight into model development by addressing issues such as classification error, class inequality, and biases. While comparisons with existing literature demonstrate the model's uses, practical considerations are also discussed. The findings suggest a significant potential to improve bone cancer diagnosis and stimulate further research on improving CNN model architecture and developing real-world deployment strategies. The training accuracy of the CNN model came out to be 94.64%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Diseases

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