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共 434 条记录，以下是1-10 订阅

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Object Tracking robot Using SIFT Based computer vision 1

Object Tracking Robot Using SIFT Based Computer Vision

引用

1st IEEE International conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Deepak Kamath, K. Rizvi, Dabir Hasan Adarsh, U. Karthik, B. Elumalai, R. CMR Institute of Technology Dept. of ECE Bengaluru India

ISBN: (纸本)9798350371802

Latterly, we have seen an immense increase in the amount of research, development and investment dedicated to the field of Autonomous Vehicle Technology or in common terms - Self-Driving Vehicles. While modern autonomous vehicles commonly use Machine and Deep Learning models using data from sensors to understand the environment and move autonomously, this project demonstrates the use of a digital image processing algorithm- SIFT, to enable a robot to track specific objects in an environment and follow them. The implementation of an efficient and innovative image tracking algorithm for autonomous vehicles is the most important outcome of this work. This paper mentions a few use cases of this work along with the potential and future scope of it. © 2024 IEEE.

关键词： computer vision

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A low-cost agriculture robot for dataset creation - Software and hardware solutions 1

A low-cost agriculture robot for dataset creation - Software...

引用

1st International conference on Innovative Engineering Sciences and Technological Research, ICIEstR 2024

作者： Kristian, Dokic Hrvoje, Kukina Hrvoje, Mikolcevic University of Osijek Faculty of Tourism and Rural Development Pozega Croatia Lira d.o.o. Lipik Development and Tourism Agency Lipik Croatia Technical School Pozega Croatia

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348637

The limited availability of public image datasets continues to be a significant obstacle for the efficient development and assessment of computer vision and machine learning algorithms in agricultural production. This paper describes a simple robotic vehicle whose goal is to collect datasets, i.e. images of plants in the early stages of development. The vehicle is designed for vertical photography of root vegetables (carrots, beets, parsley, etc.) that do not have a large leaf mass. The goal of the development is to make the production of vehicles cheap and to speed up the creation of freely accessible datasets by making such or similar vehicles. The robotic vehicle uses cheap and easily available components, and ArUco markers are used for the navigation itself, which enables simple and precise navigation. © 2024 IEEE.

关键词： Vegetables

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Neural Markov Random Field for stereo Matching

Neural Markov Random Field for Stereo Matching

引用

IEEE/CVF conference on computer vision and Pattern Recognition (CVPR)

作者： Guan, Tongfan Wang, Chen Liu, Yun-Hui Chinese Univ Hong Kong Hong Kong Peoples R China Univ Buffalo Spatial AI & Robot Lab Buffalo NY USA

ISBN: (纸本)9798350353013;9798350353006

stereo matching is a core task for many computer vision and robotics applications. Despite their dominance in traditional stereo methods, the hand-crafted Markov Random Field (MRF) models lack sufficient modeling accuracy compared to end-to-end deep models. While deep learning representations have greatly improved the unary terms of the MRF models, the overall accuracy is still severely limited by the hand-crafted pairwise terms and message passing. To address these issues, we propose a neural MRF model, where both potential functions and message passing are designed using data-driven neural networks. Our fully data-driven model is built on the foundation of variational inference theory, to prevent convergence issues and retain stereo MRF's graph inductive bias. To make the inference tractable and scale well to high-resolution images, we also propose a Disparity Proposal Network (DPN) to adaptively prune the search space of disparity. The proposed approach ranks 1st on both KITTI 2012 and 2015 leaderboards among all published methods while running faster than 100 ms. This approach significantly outperforms prior global methods, e.g., lowering D1 metric by more than 50% on KITTI 2015. In addition, our method exhibits strong cross-domain generalization and can recover sharp edges. The codes at https://***/aeolusguan/NMRF.

关键词： Markov Random Field Neural Message Passing stereo Matching Transformer

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Analysis of Automatic Recognition Algorithm of robot Visual Shape Features Based on Genetic Algorithm 1

Analysis of Automatic Recognition Algorithm of Robot Visual ...

引用

1st IEEE International conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Yu, Gongxing Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China

ISBN: (纸本)9798350323795

robot vision is an important aspect of computer science and artificial intelligence research. It mainly simulates human visual function through computer application technology, and analyzes and processes the information extracted from the image to achieve the actual detection and control effect. Firstly, this paper introduces the shape analysis method combining genetic algorithm and visual algorithm. Through the analysis of the results of the motion analysis experiment of the automatic mobile robot in linear motion, the actual theoretical speed is 0.199m/s, and the robot analyzes the turning motion of the mobile robot at a speed of 0.2m/s, and the actual turning speed is 0.2m/s, and the one-time optional turning is 360. Finally, this paper mainly analyzes and studies the feature recognition and classification methods of workpieces based on machine vision to help people better understand the classification and recognition technology of machine vision. © 2023 IEEE.

关键词： Genetic algorithms

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RGB-D Videos Human Action Recognition: A Taxonomy-Based Survey to Learn the Discriminative Features 1

RGB-D Videos Human Action Recognition: A Taxonomy-Based Surv...

引用

1st International conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Padhan, Girish Electrical & Electronics Engineering Vikash Institute of Technology Biju patnaik University of Technology Odisha Bargarh India

ISBN: (纸本)9798350361780

In computer vision human action recognition plays an important role in the modern era. Here a sensor can acquired accurately the human action and their interactions from a previously unseen data sequence. Human activity identification in video sequences is a hotspot for computer vision research owing to its practical implications. It encompasses security, surveillance, healthcare, robotics, animation, sports analysis, smart home automation and behavioral analysis. The goal of AI society is to create a system that can observe and understand human behavior and actions completely independently. For example, a robot assistant might aid a patient undergoing home monitoring by analyzing the most effective method of exercise and so avoiding additional injuries, thereby increasing the robot's usefulness to society. This kind of smart technology will be immensely useful to us since it eliminates the need for unnecessary doctor visits, lowers healthcare costs, and allows for constant remote monitoring of the patient. Many feature-based methods, both manually designed and automatically taught, have emerged in the past two decades for identifying human actions in video footage. Traditional methods of human activity identification rely on meticulously designed characteristics that drill down to the most fundamental of motions. Over the last several years, deep learning algorithms have made great progress in a number of different domains, including human location prediction, object recognition, segmentation, audio analysis, object tracking, and super-resolution. In visual recognition tasks, the deep learning model is also crucial. Instead of manually extracting the characteristics, deep learning-based methods provide a more efficient and time-saving alternative. Handcrafted features solutions were shown to be effective, however they over-relied on feature descriptors when attempting action categorization. This type of problem needed additional man hours and specialized knowledge

关键词： computer vision

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Automatic Weed Detection and Killing robot 1

Automatic Weed Detection and Killing Robot

引用

1st International conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Billa, Poornaiah Sudabathula, Seva Tharun Kasi, Divya Sree Ramichetty, Mounika Electronics and Communication Engineering Andhra Pradesh India Affiliated to JNTUK Kakinada India

ISBN: (纸本)9798350339727

Though becoming a pillar of the Indian economy, agriculture is currently having a production crisis. The issue of weeds in agriculture is a significant problem that can re- sult in substantial financial losses for farmers. Conventional methods of weed control, such as manual labor or chemical applications, can be time-consuming, demanding, and potentially harmful to the environment. To tackle these challenges, we have developed and implemented an automatic weed detection and killing robot. The robot employs computer vision algorithms to detect and categorize weeds instantly. It comprises a camera and a processing unit that captures images of the crop field and examines them to recognize weeds based on their visual features. The robot then uses a mechanical arm to administer a precise amount of herbicide to the weed, decreasing the use of chemicals and minimizing the possibility of harming non-target crops. This is accomplished by periodically taking pictures of the field, processing them on a Raspberry Pi board and using an image processing algorithm to distinguish the desired plants from the weeds based on a number of characteristics such as the size, colour, and cut. The Raspberry Pi chip sends a signal to the pesticide spraying pump to turn on when the weeds have been accurately discovered and identified through image analysis. © 2023 IEEE.

关键词： computer vision

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EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

EfficientNet-SAM: A Novel EffecientNet with Spatial Attentio...

引用

IEEE/CVF conference on computer vision and Pattern Recognition (CVPR)

作者： Farag, Ramy Upadhay, Parth Demby's, Jacket Gao, Yixiang Montoya, Katherin Garces Tousi, Seyed Mohamad Ali Omotara, Gbenga DeSouza, Guilherme Univ Missouri Dept Elect Engn & Comp Sci Vis Guided & Intelligent Robot Lab ViGIR Lab Columbia MO 65211 USA

ISBN: (纸本)9798350365474

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year's instance of the same challenge using the validation subset. It also placed 5th in this year's competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.

关键词： Attention Mechanism COVID-19 Detection EfficientNet Transformer

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Implementation of computer vision for Object Alignment Mechanism in RHI-MOLA Mobile robot as robotic Learning Platform 1

Implementation of Computer Vision for Object Alignment Mecha...

引用

1st International conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Widayaka, Parama Diptya Wardani, Ayusta Lukita Anifah, Lilik Alamsyah, Sayyidul Aulia Radimas Putra, M.D.L. Hadi, Sirojul Universitas Negeri Surabaya Dept. of Electrical Engineering Surabaya Indonesia Universitas Negeri Surabaya Dept. of Power Electronics Surabaya Indonesia Institut Teknologi Nasional Malang Dept. of Electrical Engineering Malang Indonesia Universitas Bumigora Dept. of Information Technology Mataram Indonesia

ISBN: (纸本)9798350360554

Nowadays, technology has developed rapidly that can be observed around us. Technology has had many impacts and changes in the world, for example in the industrial world. There are so many advanced technologies inside the industry such as robotics technology. With this condition, it becomes a challenge for the education sector to produce students who are competent in their fields, especially in the field of robotic. One way to increase student competency is by creating a robotics-based learning platform to increase student understanding. With this development, it is also hoped that it will be able to answer one of the agendas of Sustainable Development Goals (SDGs) number 4 Quality Education in Innovation. This article presents about application of computer vision in robotic learning platform to perform object alignment using Raspberry Pi as the unit to perform image processing and an Arduino based microcontroller to control the robot. HSV color model has been used to detect the object and PID controller has been applied in stabilizing the robot relative to the object so that the robot can maintain it position to the object. The experiment give a result of the application of computer vision with the HSV color model give a value of Hue high = 41, Hue low = 0, Saturation high = 255, Saturation low = 225, Value high = 255, and Value low = 77 to detect the object. PID controller give a good result for the robot movement with the value of kp = 2.5, ki = 0, and kd = 0.5. © 2023 IEEE.

关键词： Three term control systems

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The Development of an Intelligent robot Arm Playing chess with Human-robot Interaction 1

The Development of an Intelligent Robot Arm Playing chess wi...

引用

1st International conference on Health Science and Technology (ICHst)

作者： Thi Thoa Mac Trinh-Nhan Dam V. Thanh Tri Nguyen Ly Hoang Hiep Tien-Duc Nguyen Hanoi Univ Sci & Technol Sch Mech Engn Dept Mechatron Hanoi Vietnam

ISBN: (数字)9798350315714

ISBN: (纸本)9798350315714;9798350315721

Artificial Intelligence has grown significantly more complex and powerful than ever. Numerous top-tier techno companies compete strictly with high effort that enhances the quality of life and is making attempts to put their hands on this latest technology. Recreational activities play a significant role in every man's life, and thus, applying technology to this domain motivates development and paves the path to finding new values. AI has long been used as the engine for computation-heavy tasks, such as strategically demanding games like chess. Though the complexity of AI chess engines has reached its peak in recent years, direct face-to-face interaction between the players seems more appealing to the mate checkers. Compared to tapping on the screen, it is many times more attractive that chess players can physically go against a real-life robot in a competitive match. Such interaction would be further humanoid and further comfortable. This paper's main topic is constructing a physically capable Chess-Playing system powered by AI to compete against the player. The foundation of the Chess robot is based on the combination of mechanical mechanisms, AI engine, and 3D computer vision. The system is constructed with the model Reactor X150 robot, a 5-DOF ROS-controlled Mechanical Arm made by Troussen robotics. The powerful open-source chess engine stockfish, is the brain for computing the chess moves. The Norm algorithm detects the player's move through an attached camera by analyzing the differences between the before and after images. The robot makes the game much more interactive and builds a link between the human and computer systems.

关键词： component formatting style styling insert

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Artificial Intelligence Algorithms for robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

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1st International conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, Artificial Intelligence, and Machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： Machine vision

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