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arXiv 2023年

作者： Dong, Jiahua Cong, Yang Sun, Gan Wang, Lixu Lyu, Lingjuan Li, Jun Konukoglu, Ender The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The University of Chinese Academy of Sciences Beijing100049 China The Computer Science Department Northwestern University Evanston United States The Sony AI Tokyo108-0075 Japan The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Computer Vision Lab ETH Zurich Zürich8092 Switzerland

3D object recognition has successfully become an appealing research topic in the real-world. However, most existing recognition models unreasonably assume that the categories of 3D objects cannot change over time in the real-world. This unrealistic assumption may result in significant performance degradation for them to learn new classes of 3D objects consecutively, due to the catastrophic forgetting on old learned classes. Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects. To tackle the above challenges, we develop a novel Incremental 3D Object Recognition Network (i.e., InOR-Net), which could recognize new classes of 3D objects continuously via overcoming the catastrophic forgetting on old classes. Specifically, a category-guided geometric reasoning is proposed to reason local geometric structures with distinctive 3D characteristics of each class by leveraging intrinsic category information. We then propose a novel critic-induced geometric attention mechanism to distinguish which 3D geometric characteristics within each class are beneficial to overcome the catastrophic forgetting on old classes of 3D objects, while preventing the negative influence of useless 3D characteristics. In addition, a dual adaptive fairness compensations strategy is designed to overcome the forgetting brought by class imbalance, by compensating biased weights and predictions of the classifier. Comparison experiments verify the state-of-the-art performance of the proposed InOR-Net model on several public point cloud datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Object recognition

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Relative pose of three calibrated and partially calibrated cameras from four points using virtual correspondences

arXiv

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arXiv 2023年

作者： Tzamos, Charalambos Kocur, Viktor Barath, Daniel Haladová, Zuzana Berger Sattler, Torsten Kukelova, Zuzana Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Slovakia ETH Zürich Computer Vision and Geometry Group Switzerland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic

We study challenging problems of estimating the relative pose of three cameras and propose novel efficient solutions to the configurations (1) of four points in three calibrated cameras (the 4p3v problem), and (2) of four points in three cameras with unknown shared focal length (the 4p3vf problem). Our solutions are based on the simple idea of generating one or two additional virtual point correspondences in two views by using the information from the locations of the input correspondences. We generate such correspondences using a very simple and efficient strategy, where the new points are the mean points of three corresponding input points. The new solvers are efficient and easy to implement, since they are based on existing efficient minimal solvers, i.e., the well-known 5-point and 6-point relative pose solvers and the P3P solver. Extensive experiments on real data show that our solvers achieve state-of-the-art results. We also present a simple network that can improve the precision of the mean-point correspondences, showing the potential to learn better virtual point correspondences. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Correlate-and-excite: Real-time stereo matching via guided cost volume excitation

arXiv

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arXiv 2021年

作者： Bangunharcana, Antyanta Cho, Jae Won Lee, Seokju Kweon, In So Kim, Kyung-Soo Kim, Soohyun Mechatronics Systems and Control Laboratory KAIST Daejeon34141 Korea Republic of Robotics and Computer Vision Laboratory KAIST Daejeon34141 Korea Republic of

Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions. Recent works showed that utilization of extracted image features and a spatially varying cost volume aggregation complements 3D convolutions. However, existing methods with spatially varying operations are complex, cost considerable computation time, and cause memory consumption to increase. In this work, we construct Guided Cost volume Excitation (GCE) and show that simple channel excitation of cost volume guided by image can improve performance considerably. Moreover, we propose a novel method of using top-k selection prior to soft-argmin disparity regression for computing the final disparity estimate. Combining our novel contributions, we present an end-to-end network that we call Correlate-and-Excite (CoEx). Extensive experiments of our model on the SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate the effectiveness and efficiency of our model and show that our model outperforms other speed-based algorithms while also being competitive to other state-of-the-art algorithms. © 2021, CC BY.

关键词： Convolution

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SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects 16th

SESAME: Semantic Editing of Scenes by Adding, Manipulating o...

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16th European Conference on computer vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Kastanis, Iason Van Gool, Luc Timofte, Radu Computer Vision Lab ETH Zurich Zürich Switzerland Robotics and Machine Learning CSEM SA Alpnach Switzerland PSI ESAT KU Leuven Leuven Belgium

ISBN: (纸本)9783030585419

Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are not capable of handling the complete set of editing operations, that is addition, manipulation or removal of semantic concepts. To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects. In our setup, the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels. In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former. We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks: (a) image manipulation and (b) image generation conditioned on semantic labels. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science The Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model’s performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis.

关键词： Deep learning Systematics Image analysis Market research Skin Convolutional neural networks Lesions Global communication Medical diagnostic imaging Skin cancer

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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

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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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Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies

arXiv

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arXiv 2024年

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Aouada, Djamila Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Daejeon34113 Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data is often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection. © 2024, CC BY-NC-SA.

关键词： Anomaly detection

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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 ...

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Nonlinearity, Information and robotics, (NIR) International Conference

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

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

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.

关键词： Actuators Trajectory tracking Predictive models Control systems Numerical models Robots Predictive control

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IMU-based Parameter Identification and Position Estimation in Twisted String Actuators

IMU-based Parameter Identification and Position Estimation i...

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Simeon Nedelchev Daniil Kirsanov Igor Gaponov Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

This study proposes a technique to estimate the output state of twisted string actuators (TSAs) based on payload's acceleration measurements. We outline differential kinematics relationships of the actuator, re-formulate these into a nonlinear parameter identification problem and then apply linearization techniques to efficiently solve it as a quadratic program. Using accurate estimates of string parameters obtained with the proposed method, we can predict TSA position with sub-millimeter accuracy via conventional kinematic relationships. In addition, the proposed method supports accurate estimation under varying operating conditions, unpredictable perturbations, and poorly-excited trajectories. This technique can be employed to improve the accuracy of trajectory tracking when the use of direct position measurements is challenging, with the list of potential applications including flexible and soft robots, long-span cable robots, multi-DOF joints and others.

关键词： Actuators Parameter estimation Trajectory tracking Estimation Kinematics Soft robotics Trajectory

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence computer architecture Data models Accuracy Servers Safety

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