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共 18083 条记录，以下是131-140 订阅

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Authentication and Verification in Human-robot Cooperative robotic Cells using Stereo vision and Gesture Control

Authentication and Verification in Human-Robot Cooperative R...

引用

international Image processing, Applications and Systems conference (IPAS)

作者： Gábor Kovács Tamás Szirányi Machine Perception Research Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators’ movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation.

关键词： Automation Service robots Tracking Human-robot interaction Authentication Systems architecture User experience Real-time systems Stereo vision Safety

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vision-Based robotic Grasping: Integrating YOLOv8 and GRCNN for Dynamic Object Handling 2nd

Vision-Based Robotic Grasping: Integrating YOLOv8 and GRCNN ...

引用

2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Lyu, Xingze Hajjaj, Sami Salama Hussen School of Robotics Xi’an Jiaotong-Liverpool University 111 Taicang Avenue Jiangsu Province Suzhou Taicang215400 China School of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819639489

This work presents an approach to vision-based robotic grasping, combining scene understanding with advanced neural network architectures. The primary goal is to develop a system that enables robots to grasp a wide variety of objects in unstructured environments using RGB-D data. The methodology integrates two key components: the YOLOv8 model for real-time object detection and the Generative Residual Convolutional Neural Network (GRCNN) for precise grasp pose inference. The experimental setup employs the PyBullet simulation environment, utilizing a Universal robots UR5 model equipped with a robotiq 2F-85 parallel-jaw gripper. The YOLOv8 model is trained using a custom dataset created in PyBullet and annotated via the Roboflow platform, ensuring accurate object detection and localization. The GRCNN model generates grasp quality maps, including quality, angle, and width maps, which are crucial for determining the optimal grasp points and orientations. The system is thoroughly tested in the PyBullet environment, where it demonstrates robust performance across various scenarios, accurately detecting objects and planning grasps in real-time. The simulation results are validated through real-world experiments using the UR5 robot, confirming the system’s effectiveness in practical applications. This work also addresses the limitations of traditional grasping methods, which often rely on pre-programmed instructions and detailed 3D models, making them unsuitable for dynamic environments. By leveraging deep learning and real-time processing, the proposed system overcomes these challenges, providing a scalable and efficient solution for robotic grasping in unstructured settings. Future work will involve optimizing neural networks, exploring adaptive grasping strategies, and enhancing language understanding capabilities to improve system responsiveness and versatility in complex tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human robot interaction

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AURA: Advancing Human-robot Interaction Through Unified Voice and Gesture Control

AURA: Advancing Human-Robot Interaction Through Unified Voic...

引用

international conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Lakshya Kumar Pooja Anand Ameya Ganpatye Dept. of Electronics & Communication Engineering Amity University Greater Noida India Dept. of Electrical & Electronics Engineering MIT World Peace University Pune India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The development of artificial intelligence (AI) assistants has revolutionized human-machine interaction, but traditional systems often rely on singular input modalities such as voice commands. This project, AURA: AI robot with Unified Voice and Gesture Control, integrates voice and air gesture recognition to create a seamless and intuitive user interface. Utilizing a Raspberry Pi, camera, speaker, and microphone, the system combines computer vision and natural language processing to interpret user commands dynamically. The AI assistant is designed to respond accurately to both vocal instructions and gestures, enabling hands-free and voice-free operation in various scenarios. AURA's innovative approach enhances accessibility, convenience, and user experience, with potential applications in smart homes, healthcare, and personal robotics. This unified control mechanism demonstrates the future of multimodal interaction, emphasizing efficiency and adaptability in AI-driven systems.

关键词： Human computer interaction Computer vision robot vision systems Gesture recognition Speech recognition Smart homes Natural language processing User experience Robustness Artificial intelligence

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Depth and Dimension Estimation Using Computer vision

Depth and Dimension Estimation Using Computer Vision

引用

international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Ravindran S Srikanth S Raagul T S Madhankumar R M Ganesan M Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The growing importance of depth and dimension estimation in robotics, autonomous vehicles, and construction industries highlights the need for accurate environmental perception. Monocular depth-based cameras face challenges such as scale ambiguity and data scarcity, making precise distance measurements difficult. Stereo-vision setups provide improved scene information, thereby reducing the error rate. Deriving depth from stereo images using a Pseudo-LIDAR based approach enhances depth data. Pixel-to-length conversion from this depth information enables precise dimension estimation of objects. Developing a computer vision algorithm that calculates pixel-to-length ratios at various distances and adjusting for calibration discrepancies will streamline decision-making in autonomous systems, improving efficiency across industrial operations.

关键词： Wireless communication Computer vision Accuracy Image edge detection Depth measurement Estimation signal processing algorithms Streaming media Stereo vision Autonomous vehicles

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Designing and Manufacturing of Robo Nurse with Fully Articulating Hand

Designing and Manufacturing of Robo Nurse with Fully Articul...

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international conference on robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Sakilur Rahman Roky Habib A Hasan Md. Abu Rubayat Rokon Naimul Hasan Uday M. Tanseer Ali Dept. of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

This paper introduces a Robo Nurse designed to remotely monitor patients in hazardous environments, such as those infected with COVID-19. The robot utilizes MAX30100 and GY906 sensors to accurately measure vital parameters like body temperature, oxygen level, and heart rate. Equipped with a fully articulating hand, the robot can navigate hospital environments and perform tasks with minimal human intervention. Preliminary results demonstrate the feasibility of the proposed system for real-time remote patient care, offering a promising solution to enhance healthcare safety in high-risk areas. Future work will focus on integrating automated navigation capabilities using Raspberry Pi to further reduce human contact with patients.

关键词： Hands Temperature sensors Temperature measurement Heart rate Navigation signal processing robot sensing systems Three-dimensional printing Sensors Biomedical monitoring

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LAVViT: Latent Audio-Visual vision Transformers for Speaker Verification

LAVViT: Latent Audio-Visual Vision Transformers for Speaker ...

引用

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

作者： R. Gnana Praveen Jahangir Alam Computer Research Institute of Montreal (CRIM) Montreal Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, vision Transformers (ViTs) have shown remarkable success in various computer vision applications. In this work, we have explored the potential of ViTs, pre-trained on visual data, for audio-visual speaker verification. To cope with the challenges of large-scale training, we introduce the Latent Audio-Visual vision Transformer (LAVViT) adapters, where we exploit the existing pre-trained models on visual data without fine-tuning their parameters and train only the parameters of LAVViT adapters. The LAVViT adapters are injected into every layer of the ViT architecture to effectively fuse the audio and visual modalities using a small set of latent tokens, forming an attention bottleneck, thereby reducing the quadratic computational cost of cross-attention across the modalities. The proposed approach has been evaluated on the Voxceleb1 dataset and shows promising performance using only a few trainable parameters. Code is available at https://***/praveena2j/LAVViT

关键词： Training Visualization Computer vision Fuses Computer architecture signal processing Transformers Data models Computational efficiency Speech processing

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Method for Assessing the Efficiency of Space Debris Image Formation and processing by Orbital Computer vision Systems

Method for Assessing the Efficiency of Space Debris Image Fo...

引用

Digital signal processing and its Applications (DSPA), international conference on

作者： Ruslan S. Siryi Pavel S. Baranov Department of Television Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper proposes a method for assessing the efficiency of orbital computer vision systems (OCVS) for space debris (SD) monitoring, based on the metric of the total number of detected objects with an acceptable trajectory error for a specific OCVS as part of a spacecraft in a modeled space environment. The method incorporates models of the environment, objects, as well as the hardware and software components of the OCVS. Functional dependencies are presented, demonstrating the mutual influence of various modules included in the proposed method. The application of the method is demonstrated through the comparison of different system configurations, enabling quantitative evaluation of their efficiency and the justified optimization of system parameters.

关键词： Space vehicles Computer vision Space debris Computational modeling Orbits Software Hardware Trajectory Monitoring signal to noise ratio

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Research on MLD Modeling Method of robot Aluminum Alloy Pulsed TIG Welding Process Based on Visual Sensing

Research on MLD Modeling Method of Robot Aluminum Alloy Puls...

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Digital Analysis and processing, Intelligent Computation (DAPIC), international conference on

作者： Xiaolan Liu Yan Wang Kunpeng Zhao Rui Hao Liang Zhang Harbin Huade University Harbin Heilongjiang China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This study focuses on the hybrid logic dynamic (MLD) modeling method of robot aluminum alloy pulse TIG (Tungsten Inert Gas) welding process based on vision sensing. Welding is a key technology in modern manufacturing industry, especially in the application of aluminum alloy materials, and its welding quality control is facing complex challenges. This study aims to improve the automation and intelligence level of welding process by combining robot technology and visual sensing technology. A data acquisition system based on high-precision vision sensor and advanced image processing algorithm is designed, which realizes real-time and accurate acquisition of weld and molten pool information. The weld contour is identified by machine learning algorithm, and the geometric parameters and dynamic change characteristics of weld pool are extracted, and the statistical relationship model between welding parameters and geometric characteristics of weld pool is established. On this basis, the MLD model is constructed, which can describe the continuous and discrete dynamics in the welding process. The experimental results show that MLD model can accurately predict the change trend of weld pool geometric parameters, effectively identify the welding state, and realize accurate control of welding process. This study not only improves the stability of welding quality, but also promotes the further development of aluminum alloy welding technology, which has important practical application value and broad application prospects.

关键词： Visualization Accuracy Machine learning algorithms Welding Heuristic algorithms Process control vision sensors robot sensing systems Aluminum alloys Real-time systems

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DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance

DreamVideo: High-Fidelity Image-to-Video Generation with Ima...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Cong Gu, Jiaxi Hu, Panwen Guo, Yuanfan Dong, Xiao Xu, Hang Liang, Xiaodan School of Intelligent Engineering Sun Yat-sen University at Shenzhen Shenzhen China Noah'ark Lab Huawei Shanghai China School of Science and Engineering The Chinese University of Hong Kong at Shenzhen Shenzhen China School of Intelligent Engineering Sun Yat-sen University at Zhuhai Zhuhai China

ISBN: (纸本)9798350368741

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods frequently integrate semantic information from images or simply concatenate images, which often leads to low fidelity and flickering in the generated videos. To tackle these problems, we propose a high-fidelity image-to-video generation method by devising a frame retention branch based on a pre-trained video diffusion model, named DreamVideo. Our DreamVideo perceives the reference image via convolution layers and concatenates the features with the noisy latents as model input. By this means, the details of the reference image can be preserved to the greatest extent. In addition, by incorporating the designed double-condition classifier-free guidance, DreamVideo can generate high-quality videos of different actions by providing varying prompt texts. We conduct comprehensive experiments on the public datasets, and both quantitative and qualitative results indicate that our method outperforms the state-of-the-art method. © 2025 IEEE.

关键词： computer vision controllable video generation deep learning diffusion model Image-to-video generation

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KAN-Face: Efficient Resource Usage and Precision Lip-Sync in Talking Head Generation

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Feng, Guanwen Jin, Siyu Qian, Zhihao Li, Yunan Miao, Qiguang School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi'an Key Laboratory of Big Data and Intelligent Vision Xidian University Shaanxi Xi'an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9798350368741

Despite significant progress in NeRF-based talking head generation, problems like poor lip synchronization and inefficient resource usage remain. To solve these, we propose KAN-Face, a lightweight framework. In preprocessing, we introduce a Lip-Sync Enhancement Module that uses Wav2Lip to extract high-resolution audio features and map them to an explicit intermediate representation, ensuring precise lip movement alignment with the speaker's identity. These predicted lip-sync features are combined with fundamental audio-extracted lip features and injected into the rendering module to improve synchronization. For rendering, we introduce FastKAN to map spatial points to color values. As a variant of KAN, FastKAN's sensitivity to 3D scenes and efficient structure enable precise, fast color prediction. Our framework reduces resource consumption while enhancing lip-sync accuracy and facial reconstruction, making it ideal for talking head generation tasks in resource-limited settings. Project: https://***/KAN-Face/. © 2025 IEEE.

关键词： Face Animation KAN Network Low Resource Neural Radiance Fields Talking Head Generation

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