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共 308 条记录，以下是21-30 订阅

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Real-Time Identification of Medical Equipment Using Deep CNN and computer vision 1st

Real-Time Identification of Medical Equipment Using Deep CNN...

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1st EAI International conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Rubi, Jaya Hemalatha, R.J. Janney, Bethanney Department of Biomedical Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9783031350801

Sign language is a way of communication in which hand gestures and symbols are used to connect with each other. Communication provides interaction among people to exchange feelings and ideas. Similarly, when it comes to the handling of medical equipment using a robot, sign language should not be a barrier to carrying out such applications. The purpose of this work is to provide a real-time system that can convert Sign Language (ISL) to text format. Most of the work is based on the handcrafted feature. This paper concentrates on introducing a deep learning approach that can classify the signs using the convolutional neural network. First, we make a classifier model using the signs, then using Kera’s implementation of convolutional neural network using python we analyze those signs and identify the surgical tools. Then we process another real-time system that uses skin segmentation to find the Region of Interest in the frame. The segmented region is fed to the classifier model to predict the sign. The predicted sign would gradually identify the surgical tool and convert the sign into text. © 2023, ICst Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Surgical equipment

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Action-Conditioned Frame Prediction Without Discriminator 7th

Action-Conditioned Frame Prediction Without Discriminator

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7th International conference on Machine Learning, Optimization, and Data Science (LOD) / 1st Symposium on Artificial Intelligence and Neuroscience (ACAIN)

作者： Valencia, David Williams, Henry MacDonald, Bruce Qiao, Ting Univ Auckland Ctr Automat & Robot Engn Sci Auckland New Zealand

ISBN: (纸本)9783030954673;9783030954666

Predicting high-quality images that depend on past images and external events is a challenge in computer vision. Prior proposals have tried to solve this problem;however, their architectures are complex, unstable, or difficult to train. This paper presents an action-conditioned network based upon Introspective Variational Autoencoder (IntroVAE) with a simplistic design to predict high-quality samples. The proposed architecture combines features of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) with encoding and decoding layers that can self-evaluate the quality of predicted frames;no extra discriminator network is needed in our framework. Experimental results with two data sets show that the proposed architecture could be applied to small and large images. Our predicted samples are comparable to the state-of-the-art GAN-based networks.

关键词： Action conditioned Deep learning Frame prediction Generative models Variational Autoencoders

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An Experimental study of a Landmine Detection robot

An Experimental Study of a Landmine Detection Robot

引用

Communications and computer Science (InCCCS), International conference on

作者： Subhradip Mukherjee Eshwari M. Thilagaraj Hardik Gururaj Manish Rajpurohit Electronics and communication Department MVJ College of Engineering Bangalore India

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

A landmine detection robot's objective is to cover as much ground as it can display landmines and map the remaining region with millimeter accuracy on a visual map. The land mine detecting robot prototype model presented in this paper is user-friendly, reasonably powerful, reasonably accurate, and has a visual interface for mapping landmines, adjusting PIDs, and aligning cameras. Emphasis is placed on controlling the differential drive robot in manual, semi-auto, and auto modes. The robot's precise location is ascertained by image processing, which also gives its dead reckoning servo control live reckoning feedback. The tranquil type of sensor used to find landmines is called a beat metal detector. The user has simple yet effective control over the robot connects to the graphical user interface on the remote terminal PC. The system's overall goal is to give the user access to something strong, economical, and simultaneously understandable to the typical user.

关键词： Visualization Landmine detection Accuracy Image processing robot vision systems Prototypes Metals

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Developing an Autonomous Fruit Picking and Sorting robot for Vertical Farming 1st

Developing an Autonomous Fruit Picking and Sorting Robot for...

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1st International conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Santhosh, S. kumari, Pavitra Bindu Madhavi, J. Mohan Kumar Naik, B. Karnataka Moodbidri574225 India

ISBN: (纸本)9783031756078

The agriculture sector grapples with several challenges, such as the diminishing workforce on farms and the escalating expenses associated with manual fruit harvesting. Despite ongoing progress in automating agriculture for efficiency and large-scale production, manual labor remains prevalent in fruit harvesting. Addressing these issues necessitates a focus on labor-saving measures and the expansion of agricultural operations. To tackle these challenges, the development of an automated fruit harvesting and segregation robot emerges as a practical solution. This involves creating a robotic system that combines a low-cost vision camera and a robotic arm. The stereo-vision camera serves to identify the color, distance, and position of the fruit, while the robotic arm is employed for mechanically plucking and segregating the fruits based on ripeness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： robotic arms

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Sentinel Exploring the Effectiveness of Autonomous IoT Based Security robots in Enhancing Surveillance

Sentinel Exploring the Effectiveness of Autonomous IoT Based...

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Advanced Computing and Emerging Technologies (ACET), International conference on

作者： Ajitesh Kumar Mona Kumari Computer Engineering and Applications GLA University Mathura India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

This paper introduces an IIoT driven intelligent Security based robot "SENTINEL", leveraging an Arduino Uno, camera unit, ultrasonic sensor, motor controller, motors,The envisioned robot is engineered to autonomously survey a specified area, capturing images and videos with the camera unit. The ultrasonic sensor identifies obstacles to avert collisions, alerting the user. The robot maneuvers and alters its course through the motor controller and motors, managed by an Arduino Uno. This system finds utility in various domains, including surveillance and security, enhancing security-based system. It is economically developed, broadening its accessibility for future used. The system's deployment has undergone testing, demonstrating proficiency in recognizing and responding to environmental cues. A web- based interface facilitates remote control and monitoring by users.

关键词： Service robots Surveillance Scalability robot vision systems Motors Software Hardware Security Collision avoidance Interoperability

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Experimental Evaluation of Monocular vision-Based Obstacle Detection Methods

Experimental Evaluation of Monocular Vision-Based Obstacle D...

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Electrical, computer, Telecommunication and Energy Technologies (ECTE-Tech), International conference on

作者： Billel Nebili Rabah Louali Samir Sakhi Aimen Laidoudi Abdennour Youcefi Atmane Khellal Mehrez Boulahmar Ecole Militaire Polytechnique (EMP) Algiers Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Autonomous mobile robots require efficient and robust obstacle detection for safe and effective navigation in real-world environments. This paper explores the landscape of obstacle detection techniques, particularly monocular vision methods, which have garnered significant interest due to their affordability, lightweight nature, and rapid processing capabilities. In this work, we have investigated the performances of four practical monocular vision-based methods for obstacle detection: Canny edge detection, Shi-Tomasi corner detection, Otsu thresholding, and the You Only Look Once (YOLO) object detection model. We have evaluated these methods across diverse scenarios, considering variations in environmental conditions, camera placement, and obstacle types. The findings highlight the strengths and weaknesses of each approach, offering valuable insights for selecting appropriate techniques for robust obstacle detection.

关键词： YOLO Learning systems Deep learning Navigation Image edge detection Computational modeling robot vision systems Real-time systems Mobile robots Corner detection

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Quadruped robot System Based on Proprioceptive vision and Complex Ground Mobility Capabilities 1st

Quadruped Robot System Based on Proprioceptive Vision and Co...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Ling, Xufeng Yuan, Hao Li, Sirui Tang, Bin Shanghai Normal University Tianhua College Shanghai China Royal College of Art Shanghai China Shanghai Zhiliang Electronic Technology Co. Ltd. Shanghai China

ISBN: (纸本)9789819633487

The purpose of this paper is to explore how to improve the motion accuracy and adaptability of quadruped robots in complex environments through deep reinforcement learning techniques. A proximal policy optimization (PPO) algorithm and a gated recurrent unit (GRU) network model are adopted, and the system is simulated and trained on NVIDIA Isaac Gym simulation platform. The experimental results show that the deep reinforcement learning strategy combining the PPO algorithm and the GRU model significantly improves the accuracy and adaptability of the quadruped robot in complex environments, which provides a new idea for the application of quadruped robots in extreme scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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The First vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation 18

The First Vision For Vitals (V4V) Challenge for Non-Contact ...

引用

IEEE/CVF International conference on computer vision (ICCVW)

作者： Revanur, Ambareesh Li, Zhihua Ciftci, Umur A. Yin, Lijun Jeni, Laszlo A. Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA SUNY Binghamton Dept Comp Sci Binghamton NY 13902 USA

ISBN: (纸本)9781665401913

Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic. Remote Photoplethysmography (rPPG) - the problem of non-invasively estimating blood volume variations in the microvascular tissue from video - would be well suited for these situations. Over the past few years a number of research groups have made rapid advances in remote PPG methods for estimating heart rate from digital video and obtained impressive results. How these various methods compare in naturalistic conditions, where spontaneous behavior, facial expressions, and illumination changes are present, is relatively unknown. To enable comparisons among alternative methods, the 1st vision for Vitals Challenge (V4V) presented a novel dataset containing high-resolution videos time-locked with varied physiological signals from a diverse population. In this paper, we outline the evaluation protocol, the data used, and the results. V4V is to be held in conjunction with the 2021 International conference on computer vision(1).

关键词： Measurement computer vision Protocols Sociology Estimation Benchmark testing Photoplethysmography

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1st International conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st International conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processing, AAIMB 2023

ISBN: (纸本)9783031730672

The proceedings contain 51 papers. The special focus in this conference is on Advances in Artificial Intelligence and Machine Learning in Big Data Processing. The topics include: Topological Navigation of Path Planning Using a Hybrid Architecture in Wheeled Mobile robot;abnormal Behaviour Detection in Surveillance Videos;ISAApp – Image Based Smart Attendance Application;a Self-learning Ai-Based Information Leak Protection System;enhancing Abnormal Object Detection in Camera-Based Systems Through computer vision and Deep Learning Techniques;Detection and Classification of Brain Tumor in Magnetic Resonance Images Using CNN;diagnosis of Parkinson’s Disease Using Machine Learning and Deep Learning Techniques;a Survey on Deep Learning Based Human Activity Recognition System;a Deep Learning Approach for Non - invasive Body Mass Index Calculation;Early-stage Detection of Alzheimer’s Disease Using MRI Scans with Deep Learning;penguin Search Optimization with Deep Learning Based Cybersecurity Malware Spectrogram Image Classification;Detection and Classification of Skin Disease Using CNN;estimation of Above Ground Biomass Using Machine Learning and Deep Learning Algorithms: A Review;URL Phishing Detection Using Deep Learning and Machine Learning Techniques;enhanced Disease Recognition and Classification in Black Gram Plant Leaves Using Deep Learning;Ensemble Deep Learning Approach for Identification of DDOS Attack;ROCLT: Enhanced Text Classifier for Sentiment on Imbalanced Multiclass Tweet Data Using Hybrid Deep Learning Techniques;computer vision to Animal Footprint Classification Based on Deep Learning Model;Speech Emotion Recognition Using CNN Classifier Based on Deep Learning Model;face Detection and Recognition for Criminal Identification System Using Deep Learning.

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

1st International Conference on Pattern Analysis and Machine...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

ISBN: (纸本)9789819633487

The proceedings contain 28 papers. The special focus in this conference is on Pattern Analysis and Machine Intelligence. The topics include: Development of a Low Cost 3D LiDAR Using 2D LiDAR and Servo Motor;the Design of Machine vision-Based Waste Sorting System;ECLNet: Efficient Convolution with Lite Transformer for 3D Medical Image Segmentation;exploring High-Performance 3D Object Detection with Partial Depth Completion;full-Scale Network for Remote Sensing Object Detection;Detection of Pedestrian Movement Poses in High-Speed Autonomous Driving Environments Using DVS;city-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model;an Efficient Transformer-Based Network for Remote Sensing Image Change Detection;the Method for Three-Dimensional Visual Measurement of Circular Markers Based on Active Fusion Technology;intelligent Image Recognition and Classification Technology in Digital Media;Indoor Visible Light Positioning System Based on the Image Sensor and CNN-GRU Fusion Neural Network;stock Investor Sentiment Analysis Based on NLP;Novel Audiobook System Based on BERT;student Enrollment Consultation Q&A robot Based on Large Language Model;family Doctor Model Training Based on Large Language Model Tuning;composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection;Improved CNN-GRU RF Fingerprint Feature Recognition Method Based on Comb Filter;emotional state Recognition of English Learners Based on Deep Learning;Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis;visual Recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning;quadruped robot System Based on Proprioceptive vision and Complex Ground Mobility Capabilities;a Simulated Dataset to Evaluate the Visual-Inertial Odometry Algorithms.

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