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

共 15613 条记录，以下是611-620 订阅

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Robotic Manipulator Software and Hardware Development Using Recognition algorithms

Robotic Manipulator Software and Hardware Development Using ...

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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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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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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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HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous systems 20

HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborat...

引用

20th IEEE International conference on Mobile Ad Hoc and Smart systems, MASS 2023

作者： Anwar, Mohammad Saeid Dey, Emon Devnath, Maloy Kumar Ghosh, Indrajeet Khan, Naima Freeman, Jade Gregory, Timothy Suri, Niranjan Jayarajah, Kasthuri Ramamurthy, Sreenivasan Ramasamy Roy, Nirmalya Department of Information Systems United States Cards Umbc United States Devcom Army Research Lab United States Bowie State University Department of Computer Science United States

ISBN: (纸本)9798350324334

Gathering knowledge about surroundings and generating situation awareness for autonomous systems is of utmost importance for systems developed for smart urban and uncontested environments. For example, a large area surveillance system is typically equipped with multi-modal sensors such as cameras and LIDARs and is required to execute deep learning algorithms for action, face, behavior, and object recognition. However, these systems are subjected to power and memory limitations due to their ubiquitous nature. As a result, optimizing how the sensed data is processed, fed to the deep learning algorithms, and the model inferences are communicated is critical. In this paper, we consider a testbed comprising two Unmanned Ground Vehicles (UGVs) and two NviDIA Jetson devices and posit a self-adaptive optimization framework that is capable of navigating the workload of multiple tasks (storage, processing, computation, transmission, inference) collaboratively on multiple heterogenous nodes for multiple tasks simultaneously. The self-adaptive optimization framework involves compressing and masking the input image frames, identifying similar frames, and profiling the devices for various tasks to obtain the boundary conditions for the optimization framework. Finally, we propose and optimize a novel parameter split-ratio, which indicates the proportion of the data required to be offloaded to another device while considering the networking bandwidth, busy factor, memory (CPU, GPU, RAM), and power constraints of the devices in the testbed. Our evaluations captured while executing multiple tasks (e.g., PoseNet, SegNet, imageNet, DetectNet, DepthNet) simultaneously, reveal that executing 70% (split-ratio=70%) of the data on the auxiliary node minimizes the offloading latency by ≈ 33% (18.7 ms/image to 12.5 ms/image) and the total operation time by ≈ 47% (69.32s to 36.43s) compared to the baseline configuration (executing on the primary node). © 2023 IEEE.

关键词： Learning algorithms

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Unlocking Immersive Driving Experiences with Automotive Sensor-Powered Extended Reality (XR) 26

Unlocking Immersive Driving Experiences with Automotive Sens...

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26th Irish Machine vision and image processing conference, IMviP 2024

作者： Manila, Anushalakshmi Negi, Sonam Nowakowski, Christopher Reilhac, Patrice Valeo North America Inc. United States

ISBN: (纸本)9781837242672

While industries like healthcare and manufacturing are harnessing Extended Reality (XR) for novel applications, automotive XR opportunities remain largely untapped. XR holds immense potential beyond the current industry focus on marketing campaigns and virtual showrooms. Modern vehicles are increasingly equipped with multiple sensors for advanced driver assistance and autonomous driving. In this paper, we demonstrate how these automotive sensors and algorithms can be used to create Augmented Reality (AR) effects such as virtual object placement and occlusion, and we describe Valeo Racer, a vehicle-based XR gaming concept built using these methods https://***/jFQ8wxCANic [Valeo, 2024]. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Advanced driver assistance systems

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Finding Concrete Cracks in a Building using Improved Deep Learning algorithms 2

Finding Concrete Cracks in a Building using Improved Deep Le...

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2nd International conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Devisurya, V. Paulson, J. Seshadhri, M. Varshini, P. Nagaraj, P. Muneeswaran, V. Kongu Engineering College Department of Information Technology Erode India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350325799

To ensure functionality and durability, engineering constructions must have fractures identified and repaired. Since cracks are typically discovered through visual inspection, the examiner's personal judgment plays a vital part in both the detection of concrete cracks and the estimation of their characteristics. In the past, this has helped with both the evaluation of the crack phenomenon and the repair of various structures. The number of engineering structures has expanded as a result of industrial development, but relative to that, the level of skill in the crack-detecting field has not improved. Therefore, a quicker yet more precise method is required. The image processing method detects the cracks and their characteristics simultaneously. The formulation of the algorithm and the implementation is simple. However, other flaws, such as mistaking noises for fractures and a lack of accuracy in spotting micro-cracks, have turned into major obstacles for this technology. Finding transverse cracks in concrete structures is another critical problem. Therefore, a thorough analysis of the most recent articles is required to establish an accurate technique. In order to comprehend the potential of this technology, a critical study of the image-processing phenomenon and a thorough literature review has been done in this paper. A universal DenseNet121 classification-based algorithm with camera pictures for crack identification could be an effective strategy with greater accuracy. © 2023 IEEE.

关键词： image enhancement

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Enhancing Sign Language Recognition Using Deep Convolutional Neural Networks 14

Enhancing Sign Language Recognition Using Deep Convolutional...

引用

14th International conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Kanavos, Athanasios Papadimitriou, Orestis Mylonas, Phivos Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350318067

The advancements in sensing technologies and AI algorithms have opened up a wide range of possibilities for developing applications to meet the needs of individuals who are deaf or hard of hearing. Sign language plays a vital role in the lives of people with hearing and speaking disabilities. This research aims to explore digital image processing and machine learning methods for efficiently building a sign language dataset and creating a sign language interface system. The proposed system utilizes a Convolutional Neural Network (CNN) to analyze and interpret hand gestures and poses, converting them into natural language. The developed CNN model specifically focuses on improving the accuracy of predicting the American Sign Language alphabet. Despite variations in dataset conditions and size, the model achieved an exceptional accuracy rate of 98.73%. Additionally, it demonstrated a low loss value of 0.0539, indicating its robust performance. © 2023 IEEE.

关键词： image classification

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Deep Learning Approaches for Pneumonia Classification in Healthcare 3

Deep Learning Approaches for Pneumonia Classification in Hea...

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3rd International conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Sriram, G.K. Karunasagar, A. Sudharsan, R. Malini, A. Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350336238

In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known imageNet Computer vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of image classification was later extended to image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed. © 2023 IEEE.

关键词： Object detection

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Dog Breed Detection using YOLOv7 and WOA-CNN 10

Dog Breed Detection using YOLOv7 and WOA-CNN

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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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Classification and recognition of soybean leaf diseases in Madhya Pradesh and Chhattisgarh using Deep learning methods 2

Classification and recognition of soybean leaf diseases in M...

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2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Dixit, Shriniket Kumar, Anant Haripriya, Akash Bohre, Khitij Srinivasan, Kathiravan SCOPE Vellore India School of Bioscience and Technology Vellore India Oriental College of Science and Technology SCOPE Bhopal India Acropolis Institute of Technology SCOPE Indore India

ISBN: (纸本)9798350310719

Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. Machine vision and pattern recognition technologies can help accurately diagnose crop diseases and minimize financial losses for soybean farmers. Many research papers discuss the use of deep learning algorithms for imagebased disease detection, including for soybean crops based on CNN, SVM, KNN, etc. However, lacking a well-curated dataset for soybean diseases is a challenge. Additionally, many existing research papers focus more on demonstrating the approach's feasibility rather than providing solutions to the specific problems faced in a particular region. The proposed deep learning-based classification system for soybean leaf diseases can help identify Angular Leaf spots, Bacterial blight, Soybean Rust, and Downy mildew. An image dataset was created, and image-enhancing techniques were applied during pre-processing. The proposed classifier system achieved an efficiency of 83.9%, 93.01%, and 71.98% in classifying diseases using CNN, Resnet-V2, and KNN classifiers, respectively. © 2023 IEEE.

关键词： image enhancement

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