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检索条件"任意字段=1st International Conference on Image Processing and Robotics, ICIPRoB 2020"

共 343 条记录，以下是1-10 订阅

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2020 1st international conference on Machine Vision and image processing, MVIP 2020

2020 1st International Conference on Machine Vision and Imag...

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1st international conference on Machine Vision and image processing, MVIP 2020

ISBN: (纸本)9781728168326

The proceedings contain 56 papers. The topics discussed include: exploring the gradient for video quality assessment;an efficient approach for using expectation maximization algorithm in capsule networks;image watermarking by Q learning and matrix factorization;attention-based face antispoofing of RGB camera using a minimal end-2-end neural network;DeepFaceAR: deep face recognition and displaying personal information via augmented reality;a classified and comparative study of 2-D convolvers;class attention map distillation for efficient semantic segmentation;and monitoring wrist and fingers range of motion using leap motion camera for physical rehabilitation.

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2024 1st international conference on robotics, Engineering, Science, and Technology, REstCON 2024

2024 1st International Conference on Robotics, Engineering, ...

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1st international conference on robotics, Engineering, Science, and Technology, REstCON 2024

ISBN: (纸本)9798350348842

The proceedings contain 43 papers. The topics discussed include: NaN attacks: bit-flipping deep neural network parameters to NaN or infinity;cutting tool condition monitoring based on a machine learning approach by using vibration signals;vision transformer and residual network-based autoencoder for RGBD data processing in robotic grasping of noodle-like objects;classification of short-interfering RNA through transformer encoder model;fault diagnosis of spur gearbox by image classification using deep CNN;optimal energy management for residential with renewable energy integration;hardware design and implementation of smart energy monitoring device for smart home;human detection and human pose classification for mobile robots interaction;and vortex ring and dynamic lift generated by downstroke of elastic butterfly wing.

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image processing Technique and SVM for Epizootic Ulcerative Syndrome Fish image Classification 1st

Image Processing Technique and SVM for Epizootic Ulcerative ...

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1st international conference on Advanced Computing, Machine Learning, robotics and Internet Technologies, AMRIT 2023

作者： Chakravorty, Hitesh Das, Prodipto Department of Computer Science Assam University Silchar India

ISBN: (纸本)9783031472206

Classification is the most important tasks for Epizootic Ulcerative Syndrome (EUS) fish disease image to normal fish image. YCbCr image processing technique and SVM of machine learning is applied for image classification. Two types of image dataset EUS disease fish area versus normal fish area have been chosen for experimentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

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Fault Diagnosis of Spur Gearbox by image Classification Using Deep CNN 1

Fault Diagnosis of Spur Gearbox by Image Classification Usin...

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1st international conference on robotics, Engineering, Science, and Technology, REstCON 2024

作者： Raghav, Mahendra Singh Patel, Shivdayal PDPM Indian Insitute of Information Technology Design and Manufacturing Mechanical Engineering Jabalpur India

ISBN: (纸本)9798350348842

Fault diagnosis of two stages gearboxes using deep learning techniques has gained significant attention in the last decade. Spur gearboxes are widely used for power transmission in industries which makes the fault diagnosis study of spur gearboxes very crucial. A significant amount of work has been done to diagnose faults in spur gearbox at higher load and high speeds. However, fewer studies are performed to diagnose faults in spur gearbox running at comparatively low speeds and lower loads. In this paper, fault diagnosis of spur gearbox has been performed at relatively low speed and loading conditions using advanced signal processing techniques combined with deep convolution neural network. Four different image datasets are prepared by converting vibration signals into images using time-series plots, frequency-Amplitude plot, Hilbert-Huang Transform (HHT) spectrum and Continuous Wavelet Transmission (CWT) spectrum. The deep convolutional neural network (DCNN) has been trained by using these four different image datasets. Their accuracy and performance for identifying multiple faults in spur gear has been compared. Among these four methods, CWT-DCNN has been found to be more effective. © 2024 IEEE.

关键词： Failure analysis

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1st international conference on Advanced Computing, Machine Learning, robotics and Internet Technologies, AMRIT 2023

1st International Conference on Advanced Computing, Machine ...

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1st international conference on Advanced Computing, Machine Learning, robotics and Internet Technologies, AMRIT 2023

ISBN: (纸本)9783031472237

The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, Machine Learning, robotics and Internet Technologies. The topics include: Galactic Simulation: Visual Perception of Anisotropic Dark Matter;Protocol Anomaly Detection in IIoT;Agricultural Informatics & ICT: The Foundation, Issues, Challenges and Possible Solutions—A Policy Work;a Guava Leaf Disease Identification Application;text to image Generation Using Attentional Generative Adversarial Network;attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays;a Deep Learning Framework for Violence Detection in Videos Using Transfer Learning;multi-focus image Fusion Methods: A Review;cache Memory and On-Chip Cache Architecture: A Survey;authenticating Smartphone Users Continuously Even if the Smartphone is in the User’s Pocket;Comparative Analysis of Machine Learning Algorithms for COVID-19 Detection and Prediction;machine Learning Classifiers Explanations with Prototype Counterfactual;a Systematic study of Super-Resolution Generative Adversarial Networks: Review;stance Detection in Manipuri Editorial Article Using CRF;deep Learning Based Software Vulnerability Detection in Code Snippets and Tag Questions Using Convolutional Neural Networks;a Comprehensive study of the Performances of Imbalanced Data Learning Methods with Different Optimization Techniques;Smart Parking System Using Arduino and IR Sensor;quMaDe: Quick Foreground Mask and Monocular Depth Data Generation;fine-Grained Air Quality with Deep Air Learning;enhancing Melanoma Skin Cancer Detection with Machine Learning and image processing Techniques;image processing Technique and SVM for Epizootic Ulcerative Syndrome Fish image Classification.

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SMARTINDUstRY 2024 - Proceedings of the 1st international conference on Smart Automation and robotics for Future Industry

SMARTINDUSTRY 2024 - Proceedings of the 1st International Co...

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1st international conference on Smart Automation and robotics for Future Industry, SMARTINDUstRY 2024

The proceedings contain 16 papers. The topics discussed include: artificial intelligence for the future of construction;cobots and industrial robots;predictive maintenance for wind turbine bearings: an MLOps approach with the DIAFS machine learning model;development of an artificial intelligence tool and sensing in informatization systems of mobile robots;PCA-NuSVR framework for predicting local and global indicators of tunneling-induced building damage;design and deployment of data development toolkit in cloud manufacturing environments;research and development of image processing algorithms for effective recognition of various gestures in real time;machine learning models for the recognition of commands in smart home technologies;responsive dehydration: sensor-driven optimisation of production cycles in a solar dehydrator;and formation of the method of description and control of the relative position of the links of the upper limbs of the grip of an anthropomorphic robot.

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Comparative image processing and Object Detection in Aerial Search and Rescue 1

Comparative Image Processing and Object Detection in Aerial ...

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1st IEEE international conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Rawat, Sushil Rawat, Abhay Singh Singh Mewad, Vikas Vaidya, Himadri Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372748

Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification of potential targets. To improve the effectiveness of aircraft Search and Rescue missions, this study compares many object detection algorithms and image processing methods. The investigated object detection methods are Mask R-CNN, SSD, and YOLOv8, and image processing techniques are contrast improvement and image brightness control. The effectiveness of these strategies has been evaluated by using real aerial images that include different targets such as humans, vehicles, and fire. The results show how well the object detection algorithms work to accurately distinguish targets, and how an attractive image processing approach improves the image performance of the best object detection model, as well as how the performance of the best object detection model with image processing can improve the performance of model. This research shows that combining image processing (contrast and brightness) with an optimal object detection algorithm (Mask R-CNN) significantly improves target detection in aerial search and rescue. While only Mask R-CNN performs best, adding image processing increases its accuracy to 0.92, mAP to 0.89, and makes it more reliable in finding people in need. This shows the potential of this joint approach to save people in air rescue missions. © 2024 IEEE.

关键词： Antennas

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A study: Malignant Sino-Nasal Tumour Analysis using Deep Learning 1

A Study: Malignant Sino-Nasal Tumour Analysis using Deep Lea...

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1st IEEE international conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Joseph, Mary Duela, J Shiny Srm Institute of Science and Technology India

ISBN: (纸本)9798350372748

An uncommon sort of cancer called malignant sinonasal cancer, commonly referred to as sinusoidal carcinoma, arises in the paranasal sinuses or nasal cavity. Most sinonasal tumors occur in the maxillary sinuses located on either side of the nose or ethmoid sinuses. Deep learning techniques are utilized to analyse the Computed tomography (CT) images to determine the tumour infected area. The image data collected from either publicly available medical websites or clinically collected samples were utilized in earlier studies. These data were further processed using image processing technique to enhance the pixels for segmentation process. The study of sino nasal tumour needs more parametric evaluations to classify the type of tumour. With the aid of true positive rate, true negative rate, false positive rate and false negative rate, the performance measure may be assessed for accuracy, precision, recall, and F1score. The presented study considers various existing state-of-the-art approaches on sinonasal tumour analysis emphasizing on the different regions of infection. © 2024 IEEE.

关键词： image segmentation

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A Unified Deep Learning Approach for Integrating Retinal image Diagnosis and Surgical Tool Classification 1

A Unified Deep Learning Approach for Integrating Retinal Ima...

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1st IEEE international conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jesica, J. Anitha, J. Sujitha Juliet, D. Belfin, R.V. Karunya Institute of Technology and Sciences Dept. of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India University of North Carolina Dept. of Neurology Chapel Hill United States

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Early and accurate diagnosis by using retinal image processing is critical for enabling optimized patient care. Existing techniques for the diagnosis in medical image processing often face limitations. This research study leverages the comparative analysis of advanced Convolutional Neural Networks (CNNs), including AlexNet, InceptionV3, Xception, and EfficientNetB3;out of which EfficientNetB3 gives the comparatively higher accuracy of 84%. This research work presents a novel deep learning-based approach for detecting diseases along with the integration of Lenscraft AI. Lenscraft AI helps to segment surgical masks and identify the surgical tools used in surgical recordings using YOLOv8. The developed tool and tracking method offers a higher precision value of 0.8863 and efficiency in identifying the surgical instruments and potentially improving surgical technique for the trainee surgeons and patient safety. © 2024 IEEE.

关键词： Convolutional neural networks

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Unravelling Visual Narratives with Deep Learning for image Caption Generation 1

Unravelling Visual Narratives with Deep Learning for Image C...

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1st IEEE international conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Yoga, M. Ramyasri, M.M. Bhavatharini, N. Harini, M. NivethaSri, A. Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

image Caption Generation (ICG), situated at the confluence of computer vision and natural language processing, empowers machines to comprehend visual content and express it in human-like language. This research offers a comprehensive overview of key concepts, methodologies, and challenges in ICG. The process involves developing algorithms for the automatic generation of contextually relevant captions, utilizing deep neural networks for feature extraction, and employing natural language processing techniques for coherent composition. Recent advancements, particularly in convolutional neural networks for image processing and recurrent neural networks for language modelling, have significantly elevated the performance of image captioning systems. The study delves into the core components of an ICG system, including pre-processing techniques for image data, feature extraction mechanisms, and the integration of language models. Attention mechanisms, a key innovation in this field, enable the model to focus on relevant image regions while generating captions, closely mirroring human attention patterns. Despite notable progress, ICG faces several challenges, such as handling diverse and complex visual scenes, ensuring cross-modal coherence between images and captions, and addressing biases present in training data. Ethical considerations, particularly in applications like automated content generation, are also discussed. The study concludes by highlighting potential future directions in ICG research, including the incorporation of multimodal learning approaches, enhancing the interpretability of generated captions, and addressing societal concerns related to bias and fairness. As ICG continues to evolve, it holds promise for various applications, ranging from accessibility for the visually impaired to improving content indexing and retrieval in multimedia databases. The research also underscores the significance of the accuracy attainments, showcasing the success of the pr

关键词： Feature extraction

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