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共 18153 条记录，以下是911-920 订阅

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13th international conference on Biomimetic and Biohybrid Systems, Living Machines 2024

13th International Conference on Biomimetic and Biohybrid Sy...

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13th international conference on Biomimetic and Biohybrid Systems, Living Machines 2024

ISBN: (纸本)9783031725968

The proceedings contain 31 papers. The special focus in this conference is on Biomimetic and Biohybrid Systems. The topics include: FlyWheel: A robotic Platform for Modeling Fly Visual Behavior;a Coupled-Oscillator Model of Human Attachment Dynamics Evaluated in a robot Dyadic Interaction;creating an Artificial No-Fly Zone with Sensory Disruptions;maximizing robotic Limb Rigidity and Strain Sensing Capabilities Through Localized Kevlar Fiber Reinforcement;Flexible Strain Gauge Sensors as Real-Time Stretch Receptors for Use in Biomimetic BPA Muscle Applications;sensory Feedback Cancellation: Developing Resonator Networks to Mimic A. leptorhynchu’s Cerebellar processing of Sensory Feedback;binocular vision and Vector-Summation Based Integration of Bilateral Innate and Learned Visual Cues in Insect Navigation;bioinspired Magnetic Navigation for Exploring Celestial Bodies;bioinspired Navigation Based on Distributed Sensing in the Leech Using Dynamic Neural Fields;a Comparative Study of Reinforcement Learning and Insect-Inspired Visual Navigation Methods;erodium Awn’s Water Transport Insights for Controlled Swelling Agent Rearrangement in Anisotropic Structures;simulated Control of an Aquatic Serpentine robot with Stable Heteroclinic Channels;a Comparison of Model-Free Controllers for Trajectory Tracking in a Plant-Inspired Soft Arm;pulse Modulation in Braided Pneumatic Actuators Mimics Contractile Behavior of Biological Muscles;design of a Rat robotic Forelimb;cellular Plasticity Model for Bottom-Up robotic Design;vertical Closure Constraint for Self-replicating Machines;mechanical Design of a Feline robot for Dynamic Scaling Testing;moving Inward with Front Legs Improves Tripod Gaits for Crab-Like robot Walking in Sand;encoding 3D Leg Kinematics Using Spatially-Distributed, Population Coded Network Model;analysis Pipeline for High-Dimensional Neuromechanical Model Improvement;sequence Generator Network for Neuromechanical Control of Rat Hindlimbs.

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RMP: A Random Mask Pretrain Framework for Motion Prediction 26

RMP: A Random Mask Pretrain Framework for Motion Prediction

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IEEE 26th international conference on Intelligent Transportation Systems (ITSC)

作者： Yang, Yi Zhang, Qingwen Gilles, Thomas Batool, Nazre Folkesson, John KTH Royal Inst Technol Div Robot Percept & Learning RPL S-11428 Stockholm Sweden Scania CV AB Res & Dev S-15187 Sodertalje Sweden Mines Paris PSL F-75015 Paris France

ISBN: (纸本)9798350399462

As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we propose a framework to formalize the pretraining task for trajectory prediction of traffic participants. Within our framework, inspired by the random masked model in natural language processing (NLP) and computer vision (CV), objects' positions at random timesteps are masked and then filled in by the learned neural network (NN). By changing the mask profile, our framework can easily switch among a range of motion-related tasks. We show that our proposed pretraining framework is able to deal with noisy inputs and improves the motion prediction accuracy and miss rate, especially for objects occluded over time by evaluating it on Argoverse and NuScenes datasets.

关键词： Forecasting

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A Multi-signal Perception Network for Textile Composition Identification 48

A Multi-Signal Perception Network for Textile Composition Id...

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

作者： Peng, Bo He, Liren Wu, Dong Chi, Mingmin Chen, Jintao Shanghai Key Laboratory of Data Science Fudan University School of Computer Science Shanghai China Zhongshan PoolNet Technology Co. Ltd Zhongshan Fudan Joint Innovation Center Zhongshan China Shanghai Fabric Eyes Artificial Intelligence Technology Co. Ltd Shanghai China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China

ISBN: (纸本)9781728163277

Textile composition identification (TCI) is an essential basic link in the textile industry. Methods based on computer vision or near-infrared (NIR) signal processing have shown potential for the nondestructive TCI task. However, these methods ignore that the integration of NIR signals and visual information may help the model learn a better representation through information complementarity. This paper propose a Multi-signal Perception Network (MSPNet) for nondestructive textile composition identification, allowing the model to benefit from the advantages of multimodal data. Firstly, a two-way feature extraction network is used to obtain multi-modal features. After that, we propose a multimodal signal fusion module to control the aggregation granularity among multimodal data. Specifically, the target areas of the image are perceived by a target area perception module (TAP). Then a bi-gated aggregation (Bi-GFA) is designed to capture consistent semantic information from signal to image and image to signal. The quantitative and qualitative results of the proposed MSPNet are significantly improved compared to both single and multimodal approaches. © 2023 IEEE.

关键词： Computer vision

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Radar Based Humans Localization with Compressed Sensing and Sparse Reconstruction 24

Radar Based Humans Localization with Compressed Sensing and ...

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24th international conference on Digital signal processing, DSP 2023

作者： Eckrich, Christian Schroth, Christian A. Jamali, Vahid Zoubir, Abdelhak M. Signal Processing Group Resilient Communication Systems Technische Universität Darmstadt Germany

ISBN: (纸本)9798350339598

Localization and detection is a vital task in emergency rescue operations. Devastating natural disasters can create environments that are inaccessible or dangerous for human rescuers. Contaminated areas or buildings in danger of collapsing can be searched by rescue robots which are equipped with diverse sensors such as optical and radar sensors. In scenarios where the line of sight is blocked, e.g., by a wall, a door or heavy smoke or dust, sensors like LiDAR or cameras are not able to provide sufficient information. The usage of radar in these kinds of situations can drastically improve situational awareness and hence the likelihood of rescue. In this paper, we present a method that is used for radar imaging behind obstacles by utilizing a signal model that includes the floor reflection propagation path in addition to the direct path of the radar signal. Additionally, compressed sensing methods are presented and applied to real world radar data that was recorded by a Stepped Frequency Continuous Wave (SFCW) radar mounted on a semi-autonomous robot. The results show an improved radar image that allows the clear identification of persons behind obstacles. © 2023 IEEE.

关键词： Radar imaging

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Intelligent robot Motion Trajectory Planning and Chaotic Prediction Based on Machine vision

Intelligent Robot Motion Trajectory Planning and Chaotic Pre...

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2022 international conference on Electronics and Renewable Systems, ICEARS 2022

作者： Zhou, Xiangyang Wuchang Shouyi University Hubei Wuhan430074 China

ISBN: (纸本)9781665484251

In this paper, based on machine vision, the trajectory planning and chaos prediction of intelligent robots are studied. On the basis of the camera imaging model, the knowledge of digital image processing is used for road image preprocessing. Aiming at the phenomenon of weak edge missed detection in the edge detection process, an edge point detection algorithm based on the gradient amplitude and edge direction is proposed. On this basis of chaotic prediction and trajectory planning of the lane line. Research on robot lane changing decision model based on data mining. In view of the complex lane-changing scenes, many influencing factors, and increased requirements for the accuracy of the lane-changing model, the behavior characteristics of the robot's lane-changing behavior are analyzed. © 2022 IEEE.

关键词： Computer vision

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2023 international conference on Image processing, Computer vision and Machine Learning, ICICML 2023

2023 International Conference on Image Processing, Computer ...

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2nd international conference on Image processing, Computer vision and Machine Learning, ICICML 2023

ISBN: (纸本)9798350331417

The proceedings contain 225 papers. The topics discussed include: a novel method for aerial detection of densely occluded small targets;segmenting sonar images with an enhanced OTSU algorithm;learning a residual fusion network for SAR image despeckling;deep learning-based particle gradation detection of fillers;pre-training For mmWave radar object detection through masked image modeling;industrial inspection auxiliary system based on AR technology;metric-based few-shot learning method for driver distracted behaviors detection;exploration of lightweight neural network architectures for sentiment analysis;a solar irradiance prediction method based on signal decomposition and informer;an intelligent measurement method for air preheater clearance;helmet detection method of improved YOLOv7-tiny;and research on person object tracking method based on extended reality.

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BiEPNet: Bilateral Edge-perceiving Network for High-Resolution Human Parsing 24

BiEPNet: Bilateral Edge-perceiving Network for High-Resoluti...

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8th international conference on Digital signal processing (ICDSP)

作者： Gong, Qiqi Wei, Yunchao Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing Peoples R China

ISBN: (纸本)9798400709029

Human parsing is a fundamental task aimed at segmenting human images into distinct body parts and holds vast potential applications. Nowadays, the advancement of image-capturing devices has led to a growing number of high-resolution human images. Receptive field, detail loss and memory usage are a triplet of contradictions in high-resolution scenarios. Existing human parsing methods designed for low-resolution inputs struggle to process high-resolution images efficiently due to their massive demands for computation and memory. Some methods save resources by overwhelmingly downsampling or encoding high-resolution inputs at the cost of poor performance on details. To resolve the issues above, we propose the Bilateral Edge-Perceiving Network (BiEPNet), consisting of a resources-friendly semantic-perceiving branch to acquire sufficient global information and a simple yet effective edge-perceiving branch used to refine details. The attention mechanism is utilized to simultaneously enhance the perception of context and details, leading to better performance on the boundary regions. To verify the effectiveness of BiEPNet, we contribute a high-resolution human parsing dataset, Human4K, containing 4,000 images with more than five million pixels. Extensive experiments on Human4K demonstrate that our method effectively outperforms the state-of-the-art methods.

关键词： BiEPNet Human parsing High resolution Computer vision

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A Dual-Branch Network Based on ViT and Mamba for Semantic Segmentation of Remote Sensing Image 2

A Dual-Branch Network Based on ViT and Mamba for Semantic Se...

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2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： An, Ke Wang, Ying Chen, Liang Wang, Yupie Beijing Institute of Technology National Key Laboratory for Space-Born Intelligent Information Processing Beijing China Shanghai Academy of Spaceflight Technology Shanghai China

ISBN: (纸本)9798331515669

Semantic segmentation of remote sensing images has significant applications across various scenarios. The prevailing frameworks include Convolutional Neural Network (CNN) and Transformer. However, CNN is limited by the receptive field of convolutions, while the Transformer is constrained by computational complexity, which restricts attention calculations to local windows and fails to effectively address long-range dependency modeling. The efficient Mamba architecture, characterized by linear complexity, offers a promising solution to these challenges. Inspired by Mamba, we propose a dual-branch network based on ViT and Mamba. The vision Transformer (ViT) branch incorporates the Swin Transformer to model spatial details while maintaining computational complexity within acceptable bounds. Complementarily, the Mamba branch efficiently captures global context and long-range dependencies. Additionally, to suppress noise and conflicting information arising from the fusion of features from different frameworks, we design the Cross-Model Fusion Module (CMFM) and the Cross-Model Relevance Loss (CMRLoss) to achieve semantic consistency in the fusion process. The comprehensive experimental findings on the commonly utilized GaoFen-2 and iSAID datasets clearly illustrate the advantages of our proposed approach compared to the leading methods in the field. © 2024 IEEE.

关键词： Semantic Segmentation

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Advanced Pothole Detection Using Image processing

Advanced Pothole Detection Using Image Processing

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2024 international conference on signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Suhas, A.R. Girijalakshmi Pavan, T.A. Sandeep Reddy, P. Shreyas, B.S. Mahendra, M. HKBK College of Engineering Department of ECE Karnataka Bangalore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

Road transport is the most common mode of transport worldwide, so regulatory authorities should pay more attention to the safety of passengers. Despite the various causes of road safety problems, poorly maintained roads are important, as potholes appear to be a major cause of accidents worldwide. This paper proposes a comprehensive approach to identifying potholes, autonomous filling of them, and maintaining a database with constant updates on the potholes and their clearance to prevent accidents and improve road safety. A web-based portal reports the potholes to concerned authorities by sending the location, size, depth, and predictive analytics to anticipate pothole-related issues. Implemented as a monocular vision autonomous car prototype using an Arduino UNO as a processing chip. This paper highlights the importance of proactive measures to enhance road safety in addition to pothole prevention. Regulators are urged to invest in remediation and technical improvements to reduce the risks associated with poor roads. Together, we may assist in reducing accidents and improving overall road safety by encouraging good driving practices and citizen participation in reporting accidents on the roads. However, combined with the latest innovations like predictive analytics and automated vehicle technology, this collaborative approach has a chance to build safer routes for communities across the world. © 2024 IEEE.

关键词： Predictive analytics

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LGVIT: Local-Global vision Transformer for Breast Cancer Histopathological Image Classification 48

LGVIT: Local-Global Vision Transformer for Breast Cancer His...

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

作者： Wang, Lang Liu, Juan Jiang, Peng Cao, Dehua Pang, Baochuan School of Computer Science Wuhan University Institute of Artificial Intelligence Wuhan China Landing Artificial Intelligence Center for Pathological Diagnosis Wuhan China

ISBN: (纸本)9781728163277

Breast cancer histopathological image classification has made great progress with the use of Convolutional Neural Networks (CNNs). However, due to the limited receptive field, CNNs have difficulty in learning the global information of breast cancer histopathological images, hindering the further improvement of this task. To solve this problem, we reasonably apply self-attention mechanism to this task and propose a new network called Local-Global vision Transformer (LGViT) which utilizes CNNs to capture local features and self-attention mechanism to learn global features of histopathological images. LGViT has several advantages: (1) We propose Local-Global Multi-head Self-attention, a new mechanism that models long-range dependencies with low computational cost. In this mechanism, self-attention is first performed separately within each window. Then, Multiple Instance Learning scheme is utilized to obtain a representative token for each window. Finally, we compute self-attention among these representative tokens to capture global information. (2) We propose Ghost Feed-forward Network, which compensates for the deficiency of vision Transformer in capturing local features via a locality mechanism. (3) We use a CNN stem to effectively capture low-level information. Experiments on the PatchCamelyon dataset show that LGViT is better than other state-of-the-art methods. © 2023 IEEE.

关键词： Convolutional neural networks

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