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arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks

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LiCAR: pseudo-RGB LiDAR image for CAR segmentation

arXiv

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arXiv 2025年

作者： de Loyola Páez-Ubieta, Ignacio Velasco-Sánchez, Edison P. Puente, Santiago T. AUtomatics RObotics and Artificial Vision Lab IUII: University Institute for Computer Research University of Alicante Crta. San Vicente s/n San Vicente del Raspeig AlicanteE-03690 Spain

With the advancement of computing resources, an increasing number of Neural Networks (NNs) are appearing for image detection and segmentation appear. However, these methods usually accept as input a RGB 2D image. On the other side, Light Detection And Ranging (LiDAR) sensors with many layers provide images that are similar to those obtained from a traditional low resolution RGB camera. Following this principle, a new dataset for segmenting cars in pseudo-RGB images has been generated. This dataset combines the information given by the LiDAR sensor into a Spherical Range Image (SRI), concretely the reflectivity, near infrared and signal intensity 2D images. These images are then fed into instance segmentation NNs. These NNs segment the cars that appear in these images, having as result a Bounding Box (BB) and mask precision of 88% and 81.5% respectively with You Only Look Once (YOLO)-v8 large. By using this segmentation NN, some trackers have been applied so as to follow each car segmented instance along a video feed, having great performance in real world experiments. © 2025, CC BY.

关键词： Image segmentation

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Controllable Segmentation-Based Text-Guided Style Editing

arXiv

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arXiv 2025年

作者： Li, Jingwen Chandrasekar, Aravind Rocha, Mariana Li, Chao Chen, Yuqing Department of Computer Science Fudan University China School of AI and Robotics AuroraTech Labs United States Vision Intelligence Group Federal University of Rio de Janeiro Brazil Dept. of Computer Science Eastern Asia Institute of Technology Beijing China

We present a novel approach for controllable, region-specific style editing driven by textual prompts. Building upon the state-space style alignment framework introduced by StyleMamba, our method integrates a semantic segmentation model into the style transfer pipeline. This allows users to selectively apply text-driven style changes to specific segments (e.g., "turn the building into a cyberpunk tower") while leaving other regions (e.g., "people" or "trees") unchanged. By incorporating region-wise condition vectors and a region-specific directional loss, our method achieves high-fidelity transformations that respect both semantic boundaries and user-driven style descriptions. Extensive experiments demonstrate that our approach can flexibly handle complex scene stylizations in real-world scenarios, improving control and quality over purely global style transfer methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly

arXiv

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arXiv 2025年

作者： Ghimpeteanu, Gabriela Rajani, Hayat Quintana, Josep Garcia, Rafael Coronis Computing SL University of Girona Science and Technology Park Carrer Pic de Peguera 11 Edifici Giroemprèn Girona17003 Spain Computer Vision and Robotics Research Institute University of Girona Campus Montilivi Edifici P4 Girona17003 Spain

Ensuring food safety and quality is critical in the food processing industry, where the detection of contaminants remains a persistent challenge. This study presents an automated solution for detecting foreign objects on pork belly meat using hyperspectral imaging (HSI). A hyperspectral camera was used to capture data across various bands in the near-infrared (NIR) spectrum (900-1700 nm), enabling accurate identification of contaminants that are often undetectable through traditional visual inspection methods. The proposed solution combines pre-processing techniques with a segmentation approach based on a lightweight vision Transformer (ViT) to distinguish contaminants from meat, fat, and conveyor belt materials. The adopted strategy demonstrates high detection accuracy and training efficiency, while also addressing key industrial challenges such as inherent noise, temperature variations, and spectral similarity between contaminants and pork belly. Experimental results validate the effectiveness of hyperspectral imaging in enhancing food safety, highlighting its potential for broad real-time applications in automated quality control processes. © 2025, CC BY-NC-SA.

关键词： Belt conveyors

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Toward Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

IEEE Transactions on Medical Robotics and Bionics

引用

IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 768-781页

作者： Long Lei Yingbai Hu Zixing Jiang Juzheng Miao Xiao Luo Yu Zhang Qiong Wang Shujun Wang Zheng Li Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Multi-Scale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China Department of Surgery The Chinese University of Hong Kong Hong Kong China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong China Department of Surgery Chow Yuk Ho Technology Centre for Innovative Medicine Li Ka Shing Institute of Health Sciences and Multiscale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system’s feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning.

关键词： Ultrasonic imaging Lungs Probes Robots Robot kinematics Medical robotics Ribs Medical services Surface morphology Computed tomography

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

引用

IEEE Transactions on Wireless Communications 2025年

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Semantic SLAM for an AUV using object recognition from point clouds

引用

IFAC-PapersOnLine 2018年第29期51卷 360-365页

作者： Himri, K. Ridao, P. Gracias, N. Palomer, A. Palomeras, N. Pi, R. Computer Vision and Robotics Institute Universitat de Girona Pic de Peguera 13 Girona17003 Spain

This paper presents a navigation and mapping system for an autonomous underwater vehicle (AUV) while operating near a man-made underwater environment. The objective is to recognize objects (or object parts) and use these as landmarks for simultaneous localization and mapping (SLAM). This approach is intended as the first step towards autonomous object manipulation, to be carried out at a later stage. The approach contains two main components: Object recognition from range data, and feature-based semantic SLAM. For the first component we propose an automatic method for the recognition and location of 3D objects using 3D point clouds as input, extracted from a laser scanner. Since it is common in inspection maintenance and repair (IMR) applications to have access to the 3D models of the objects of interest, the proposed method assumes a priori knowledge of the 3D models of these objects. In typical man-made environments, such objects can be distinct components of a structure, such as valves, pipes and wet-mateable connectors. The object recognition is based on recently proposed global descriptors for point clouds, that allow a compact description of the object shape, which is independent of the object view point. Once an object is recognized, its pose with respect to the AUV is determined using an ICP-based method. The second component of the approach is a feature based SLAM algorithm that uses the recognized objects as landmarks to improve the AUV navigation. The paper presents preliminary results obtained with the Girona 500 AUV, equipped with a fast laser scanner recently developed at the University of Girona. Tests conducted in a controlled environment (water tank) illustrate the suitability of the approach. © 2018

关键词： Autonomous underwater vehicles Air navigation Content based retrieval Laser applications Mapping Object recognition Repair robotics Scanning Semantics Three dimensional computer graphics Water tanks 3d object recognition Autonomous underwater vehicles (AUVs) Descriptors Laser scanner Mapping (SLAM) Shape functions Simultaneous localizations Viewpoint Features Histogram (VFH)

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Towards Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

IEEE Transactions on Medical Robotics and Bionics

引用

IEEE Transactions on Medical robotics and Bionics 2025年

作者： Lei, Long Hu, Yingbai Jiang, Zixing Miao, Juzheng Luo, Xiao Zhang, Yu Wang, Qiong Wang, Shujun Li, Zheng Heng, Pheng-Ann The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Shenzhen Institute of Advanced Technology Shenzhen China The Chinese University of Hong Kong Multi-scale Medical Robotics Centre Ltd Hong Kong Hong Kong The Chinese University of Hong Kong Department of Surgery Hong Kong Hong Kong Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong CAS Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Shenzhen China The Hong Kong Polytechnic University Department of Biomedical Engineering Hong Kong Hong Kong Chinese University of Hong Kong Li Ka Shing Institute of Health Sciences Multiscale Medical Robotics Centre Ltd Department of Surgery Chow Yuk Ho Technology Centre for Innovative Medicine Hong Kong Hong Kong Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system's feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning. © 2018 IEEE.

关键词： Motion planning

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Preliminary Study on Slack-Free Model Predictive Control of Twisted String-Based Antagonistic Joints

Preliminary Study on Slack-Free Model Predictive Control of ...

引用

2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Balakhnov, Oleg Nedelchev, Simeon Gaponov, Igor Innopolis University Institute of Robotics and Computer Vision Innopolis Russia

ISBN: (纸本)9781728187631

In this work, we propose a model predictive controller (MPC) for simultaneous position and tension tracking in twisted string actuator-based antagonistic joints. The proposed controller takes into account unidirectional nature of the cables and minimize trajectory tracking error as well as control effort. The approach has been evaluated numerically in a series of simulations that demonstrated its feasibility and highlighted its advantage over the unconstrained controller, with position tracking accuracy improved by a factor of 10. The proposed MPC can be generalized and serve as the starting point for antagonistic control in multiple-degree-of-freedom systems based on twisted string actuators. © 2020 IEEE.

关键词： Actuators

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The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection

arXiv

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arXiv 2025年

作者： Saha, Anup Adeola, Joseph Ferrera, Nuria Mothershaw, Adam Rezze, Gisele Gaborit, Séraphin D’Alessandro, Brian Hudson, James Szabó, Gyula Pataki, Balazs Rajani, Hayat Nazari, Sana Hayat, Hassan Primiero, Clare Soyer, H. Peter Malvehy, Josep Garcia, Rafael Computer Vision and Robotics Research Institute University of Girona Girona Spain Dermatology Department Hospital Clínic Barcelona Universitat de Barcelona Barcelona Spain Frazer Institute The University of Queensland Dermatology Research Center Brisbane Australia ISAHIT Paris France Canfield Scientific Inc. ParsippanyNJ United States V7 London United Kingdom HUN-REN Institute for Computer Science and Control Budapest Hungary CIBER de Enfermedades Raras Instituto de Salud Carlos III Barcelona Spain Dermatology Department Princess Alexandra Hospital BrisbaneQLD Australia Dermatology Department University of Trieste Trieste Italy

Artificial intelligence has significantly advanced skin cancer diagnosis by enabling rapid and accurate detection of malignant lesions. In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the center of the image. While these lesion-centric datasets have been fundamental for developing diagnostic algorithms, they lack the context of the surrounding skin, which is critical for improving lesion detection. The iToBoS dataset was created to address this challenge. It includes 16,954 images of skin regions from 100 participants, captured using 3D total body photography. Each image roughly corresponds to a 7×9 cm section of skin with all suspicious lesions annotated using bounding boxes. Additionally, the dataset provides metadata such as anatomical location, age group, and sun damage score for each image. This dataset aims to facilitate training and benchmarking of algorithms, with the goal of enabling early detection of skin cancer and deployment of this technology in non-clinical environments. © 2025, CC BY-NC-SA.

关键词： Lung cancer

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