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检索条件"任意字段=International Conference on Robot Vision and Signal Processing"

共 18125 条记录，以下是491-500 订阅

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MAKING THE UNKNOWN MORE CERTAIN: A STACKED ENSEMBLE CLASSIFIER FOR OPEN GESTURE RECOGNITION WITH A SOCIAL robot 47

MAKING THE UNKNOWN MORE CERTAIN: A STACKED ENSEMBLE CLASSIFI...

引用

47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Brock, Heike Gomez, Randy Honda Res Inst Japan Co Ltd 8-1 Honcho Wako Saitama 3510188 Japan

ISBN: (纸本)9781665405409

We introduce a novel stacked ensemble classifier for the unconstrained recognition of known and unknown gestural input data in nonverbal communication with a social robot. The architecture utilizes three separate CNNs of different expected data input size and combines their output predictions to a unified estimate. Analysis shows that in comparison to a single CNN architecture, the combined estimate reduces prediction confidence values for unknown gestural movement segments, making the system able to identify unknown data input with higher certainty under both laboratory and real environment conditions. In a human-robot interaction experiment, we are able to improve unknown class detection accuracy by up to 40% under maintained or equal known class recognition performance, and hence considerably enhance the overall robustness of the recognition system.

关键词： open gesture recognition ensemble classifier nonverbal communication human-robot interaction social robots

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Mobile robot Path Planning Based on Improved Smooth A∗ Algorithm and Optimized Dynamic Window Approach 2

Mobile Robot Path Planning Based on Improved Smooth A∗ Algo...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Wu, Dongsheng Li, Yipeng Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350368888

To address the issues of low search efficiency, redundant nodes, and poor smoothness associated with the traditional A∗ algorithm in robotic path planning, this paper proposes a robot path planning algorithm that integrates an improved smooth A∗ algorithm with the Dynamic Window Approach (DWA). A dynamic weight factor is introduced into the cost function of the A∗ algorithm's heuristic function to enhance path search efficiency. The global path is optimized for redundancy using the Floyd algorithm, reducing the number of turns. The optimized global path is then further processed for path circular arc smoothing, effectively eliminating rough turns and improving the overall smoothness of the path. The evaluation function of the DWA algorithm is optimized, combining the improved A∗ algorithm with the Dynamic Window Approach. The global path's key nodes serve as target points for the DWA algorithm, enabling segmented path planning. Simulation results demonstrate that the improved A∗ algorithm effectively overcomes the deficiencies of the traditional A∗ algorithm, and robots using the integrated algorithm can closely adhere to the globally optimal path, exhibiting improved obstacle avoidance performance in the presence of unknown obstacles. © 2024 IEEE.

关键词： Cost functions

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A review of binocular vision-based target detection techniques

A review of binocular vision-based target detection techniqu...

引用

2023 international conference on Image, signal processing, and Pattern Recognition, ISPP 2023

作者： Lu, Hao Dong, Yu School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou Gansu China

ISBN: (纸本)9781510666351

Binocular vision-based target detection is one of the hot topics in computer vision, where the technique aims to detect and localize target objects in images. The technology has applications in fields such as autonomous driving, video surveillance, and UAV flight control. In recent years, with the development of deep learning techniques, its speed, accuracy, and robustness have led to its widespread use in various research areas. This paper first lists the history of the development of target detection techniques, then introduces two target detection methods for binocular vision, and finally suggests possible improvements and development trends. Through summary and analysis, the aim is to provide a reference for work related to conducting binocular vision target detection. © 2023 SPIE.

关键词： Binocular vision

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DEEP LEARNING EEG TECHNOLOGY DEVELOPMENT AND BRAIN COMPUTER INTERFACE CHIPS PROGRESS 49

DEEP LEARNING EEG TECHNOLOGY DEVELOPMENT AND BRAIN COMPUTER ...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Li, Wenjie Chen, Lei Lv, Bing Li, Xiaobo Wang, Weijun Xu, Kun Ding, Xilun Beihang Univ Sch Mech Engn & Automat Beijing 10086 Peoples R China Beijing Microelect Technol Inst Beijing 100076 Peoples R China China Acad Aerosp Elect Technol Beijing 100094 Peoples R China China Elect Technol Grp Corp Res Inst 21 Robot Engn Ctr Shanghai 200233 Peoples R China

ISBN: (纸本)9798350374520;9798350374513

Brain Computer Interfaces establishes an interaction mode between the human brain and external devices, and has broad application prospects. With the development of neuroscience and artificial intelligence technology, brain computer interfaces have rapidly evolved from a single perception stage to a multimodal cognition and complex interaction control stage. This article elaborates on the functional zoning of the brain and cerebellum and the latest research on brain signal classification. Afterwards, the latest research progress of deep learning based EEG signal processing technology was analyzed in depth from three key directions: brain network analysis, research on neurological diseases, cognitive analysis, and emotion recognition. This paper provides a detailed description of the typical structure and main functional unit design ideas of Brain Computer Interface chips, and analyzes the core and difficult technologies from the aspects of acquisition, 3D heterogeneous integration, low-power design, software and brain science research. Finally, based on typical Brain Computer Interface design and application cases, the future development trends of Brain Computer Interface chips and deep learning EEG technology are proposed.

关键词： Brain Computer Interface Microsystem Deep Learning

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Machine vision based automated 3-DOF Articulated robot for fruit defect Identification and Segregation 14

Machine Vision based automated 3-DOF Articulated Robot for f...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ramkumar, S. Venusamy, Kanagaraj Eswaran, A. Jeevitha, N. Ramyapriya, G. Kannadhasan, S. Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering thandalam Chennai India Roever Engineering College Department of Electrical and Electronics Engineering Perambalur India Study World College of Engineering Department of Electronics and Communication Engineering Coimbatore Chennai India

ISBN: (纸本)9798350335095

The automation scenario in the current industrial as well as domestic applications has seen an exponential growth over the decade. robot plays an important role in industrial automation but in some cases, it needs some extent of human intervention in quality inspection. This paper focuses on solving problems related to defect identification and segregation of fruits using 3-DOF articulated robot configuration with the integration of machine vision system and deep learning algorithms. To perceive the features of object, A USB camera is utilized. The machine vision algorithm will next use a variety of methods for processing digital images to extract the relevant data and decide whether to continue processing the product, redirect it to a different stage of production, or just throw it away. In recent days, the advancement in deep learning leads to the phenomenal growth in computer vision. There are many state-of-the-art object detection algorithms available in deep learning technology and we are using Inception-V3 algorithm that can be used for the machine vision operation in our solution. Based on the results from the algorithm, segregation operation is carried out successfully. © 2023 IEEE.

关键词： Fruits

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Development of Deep Learning-Based Laser Weeding robot 11

Development of Deep Learning-Based Laser Weeding Robot

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11th IEEE international conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Huy, Kien Bui Van, Thang Nguyen Duc, Hieu Nguyen Minh, Hieu Dang Vietnam Japan Univiersity Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9798350350142

Agriculture is an important sector that contributes significantly to Vietnam's GDP. The use of pesticides and herbicides, however, raises great concern due to their harm to the environment and human health. The government is taking action to eliminate the use of these chemicals, meanwhile, alternative solutions are needed. The aim of this paper is to develop an effective solution for weed control using a computer vision-based laser weed-killing robot. To ensure efficiency and accuracy, computer vision models must be evaluated based on different metrics such as performance, speed, and computational cost represented by mean average precision, speed of inferences (in fps), and the size of the model in FLOPs and params. The study focuses on deploying the state-of-the-art object detection model YOLOv8 on a dataset of 9,000 labeled images taken from fields in Pakistan. YOLOv8 outperforms YOLOv5 in a previous study with an accuracy of 0.9043@IOU 0.5, particularly suited for hardware efficient integration due to its 'n' and 's' versions. Both models in 'n' version demonstrated similar real-time frame processing times, operating at an FPS rate of approximately 62.5ms using the same computer configuration. The results show that YOLOv8n is a fitting choice for the laser weedkilling robot due to its superior weed detection capabilities. © 2024 IEEE.

关键词： Computer vision

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Audio-Visual Speaker Diarization in the Framework of Multi-User Human-robot Interaction 48

Audio-Visual Speaker Diarization in the Framework of Multi-U...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Dhaussy, Timothee Jabaian, Bassam Lefevre, Fabrice Horaud, Radu Avignon University LIA-CERI France Inria at Université Grenoble Alpes France

ISBN: (纸本)9781728163277

The speaker diarization task answers the question "who is speaking at a given time?". It represents valuable information for scene analysis in a domain such as robotics. In this paper, we introduce a temporal audio-visual fusion model for multiusers speaker diarization, with low computing requirement, a good robustness and an absence of training phase. The proposed method identifies the dominant speakers and tracks them over time by measuring the spatial coincidence between sound locations and visual presence. The model is generative, parameters are estimated online, and does not require training. Its effectiveness was assessed using two datasets, a public one and one collected in-house with the Pepper humanoid robot. © 2023 IEEE.

关键词： Anthropomorphic robots

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Design of Dual robot Collaborative Assembly Scheme for Aerospace Products 10th

Design of Dual Robot Collaborative Assembly Scheme for Aeros...

引用

10th international conference on signal and Information processing, Network and Computers, ICSINC 2022

作者： Zhang, Lijian Pan, Dingwen Hu, Ruiqin Beijing Institute of Spacecraft Environment Engineering Beijing100094 China Beijing Engineering Research Center of Intelligent Assembly Technology and Equipment for Aerospace Product Beijing100094 China

ISBN: (纸本)9789811999673

For the automatic installation requirements of spacecraft components, an automatic assembly scheme based on the cooperation of dual robots A large load robot is used to grab large weight components, and accurately transport it to the installation location. A small load robot is used for the fastener installation between the component and the spacecraft cabin. The large load robot is equipped with a flexible gripping end effector, which can be adapted to spacecraft component with different size. An indoor positioning system is used to realize the coarse positioning between the various parts of the system, and An indoor positioning system is used to realize the coarse positioning between the various parts of the system, and the precise positioning of the components and screw holes can be realized by robot vision. The designed scheme can provide reference for the automation process for China's spacecraft development. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D modeling

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GMML IS ALL YOU NEED 30

GMML IS ALL YOU NEED

引用

30th IEEE international conference on Image processing (ICIP)

作者： Atito, Sara Awais, Muhammed Nandam, Srinivasa Kittler, Josef Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford Surrey England Univ Surrey Surrey Inst People Centred AI Guildford GU2 7XH Surrey England

ISBN: (纸本)9781728198354

vision transformers (ViTs) have generated significant interest in the computer vision community because of their flexibility in exploiting contextual information, whether it is sharply confined local, or long range global. However, they are known to be data hungry and therefore often pretrained on large-scale datasets, e.g. JFT-300M or ImageNet. An ideal learning method would perform best regardless of the size of the dataset, a property lacked by current learning methods, with merely a few existing works studying ViTs with limited data. We propose Group Masked Model Learning (GMML), a self-supervised learning (SSL) method that is able to train ViTs and achieve state-of-the-art (SOTA) performance when pre-trained with limited data. The GMML uses the information conveyed by all concepts in the image. This is achieved by manipulating randomly groups of connected tokens, successively covering different meaningful parts of the image content, and then recovering the hidden information from the visible part of the concept. Unlike most of the existing SSL approaches, GMML does not require momentum encoder, nor relies on careful implementation details such as large batches and gradient stopping. Pretraining, finetuning, and evaluation codes are available under: https://***/GMML.

关键词： Self-supervised Learning vision Transformers Group Masked Model Learning Deep Learning

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Research on Classification Identification of Surface EMG signals Based on Deep Learning 5

Research on Classification Identification of Surface EMG Sig...

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5th international conference on Artificial Intelligence and Computer Engineering

作者： Lu, Yumeng Ling, Long Zhang, Zhenjie Deng, Jingping Zhuhai Coll Sci & Technol Sch Intelligent Mfg & Aeronaut Zhuhai Guangdong Peoples R China

ISBN: (纸本)9798331528911;9798331528928

With the continuous development of robot technology in the field of industrial production, the introduction of biological electrical signals to control robots will not only provide a new way of human-computer interaction operation, but also improve the security and stability of the system. Among them, SEMG has a high signal-to-noise ratio, and the robot control accuracy based on SEMG is higher compared to other bioelectrical signals. This paper studies the classification and identification method of sEMG signals. Classified and of signals by constructing a one-dimensional convolutional neural network model. By studying the one-dimensional convolutional neural network from the horizontal and vertical dimensions respectively, the experimental results show that the accuracy of the horizontal dimension recognition is higher, which can reach 93.28%.

关键词： Surface electromyogram signal signal processing Classification identification Convolutional neural network

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