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arXiv 2024年

作者： Meraz, Md. Ansari, Md Afzal Javed, Mohammed Chakraborty, Pavan Center of Intelligent Robotics Lab Department of Information Technology Computer Vision & Biometrics Lab Department of Information Technology Indian Institute of Information Technology Allahabad U.P Prayagraj India

In recent years, the challenge of 3D shape analysis within point cloud data has gathered significant attention in computer vision. Addressing the complexities of effective 3D information representation and meaningful feature extraction for classification tasks remains crucial. This paper presents Point-GR, a novel deep learning architecture designed explicitly to transform unordered raw point clouds into higher dimensions while preserving local geometric features. It introduces residual-based learning within the network to mitigate the point permutation issues in point cloud data. The proposed Point-GR network significantly reduced the number of network parameters in Classification and PartSegmentation compared to baseline graph-based networks. Notably, the Point-GR model achieves a state-of-the-art scene segmentation mean IoU of 73.47% on the S3DIS benchmark dataset, showcasing its effectiveness. Furthermore, the model shows competitive results in Classification and Part-Segmentation tasks. © 2024, CC BY.

关键词： Graph neural networks

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Convected activation profiles and the measurement of visual motion

Convected activation profiles and the measurement of visual ...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： A.M. Waxman J. Wu F. Bergholm Laboratory for Sensory Robotics College of Engineering Boston University Boston MA USA Computer Vision and Graphics Laboratory Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

A method is developed for the measurement of short-range visual motion in image sequences, making use of the motion of image features such as edges and points. Each feature generates a Gaussian activation profile in a spatiotemporal neighborhood of specified scale around the feature itself; this profile is then convected with motion of the feature. The authors show that image velocity estimates can be obtained from such dynamic activation profiles using a modification of familiar gradient techniques. The resulting estimators can be formulated in terms of simple ratios of spatiotemporal filters (i.e. receptive fields) convolved with image feature maps. A family of activation profiles of varying scale must be utilized to cover a range of possible image velocities. They suggest a characteristic speed normalization of the estimate obtained from each filter in order to decide which estimate is to be accepted. They formulate the velocity estimators for dynamic edges in 1-D and 2-D image sequences, as well as that for dynamic feature points in 2-D image sequences.< >

关键词： Motion measurement Image sequences Motion analysis Filters Laboratories computer vision Robot sensing systems Educational institutions computer graphics Numerical analysis

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Magnetic-visual sensor fusion based medical SLAM for endoscopic capsule robot

arXiv

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arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Gilbert, Hunter Araujo, Helder Konukoglu, Ender Sitti, Metin Max Planck Institute for Intelligent Systems Stuttgart Germany Computer Vision Laboratory Eth Zurich Computer Engineering Department Bogazici University Louisiana State University Baton RougeLA United States Robotics Laboratory University of Coimbra

A reliable, real-time simultaneous localization and mapping (SLAM) method is crucial for the navigation of actively controlled capsule endoscopy robots. These robots are an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a dense, non-rigidly deformable, and real-time map fusion approach for actively controlled endoscopic capsule robot applications. The method combines magnetic and vision based localization, and makes use of frame-to-model fusion and model-to-model loop closure. The performance of the method is demonstrated using an ex-vivo porcine stomach model. Across four trajectories of varying speed and complexity, and across three cameras, the root mean square localization errors range from 0.42 to 1.92 cm, and the root mean square surface reconstruction errors range from 1.23 to 2.39 cm. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetism

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Millimeter-wave radar image analysis for the traffic sensing 2

Millimeter-wave radar image analysis for the traffic sensing

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2nd Spring International Conference on Defence Technology, ICDT 2020

作者： Wu, H.M. Qi, F. Wang, J.K. Wang, Y. School of Communication Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

Millimeter-wave(MMW) radar sensing is one of the most promising technologies to provide safe navigation for autonomous vehicles due to its expected high-resolution imaging capability However, driverless cars have higher request for different environment and light conditions. Therefore, millimetre-wave imaging is of paramount importance for complex load scenario. In this paper, we have built models of pavement pits and bulges and analysed their with differences ways of antennas. A comparison of the imaging performance of experimental systems operating at a MMW radar and a Lidar is presented with the analysis of features for initial image interpretation Experimental images of the complex road surface are made by a 94GHz frequency-modulated continuous-wave (FMCW) radar technique with 3mm wavelength. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Inspecting Mega Solar Plants through computer vision and Drone Technologies

Inspecting Mega Solar Plants through Computer Vision and Dro...

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Frontiers of Information Technology (FIT)

作者： Syed Umaid Ahmed Muhammad Affan Muhammad Ilyas Raza Muhammad Harris Hashmi Department of Computer Sciences National University of Computer and Emerging Sciences Karachi Pakistan SeaTech School of Engineering University of Toulon La Garde France Institute of Robotics and Computer Vision Innopolis University Innopolis Russia Department of Computer Science University of Alabama at Birmingham Birmingham United States

ISBN: (纸本)9798350345940

This research presents a unique approach for monitoring the large-scale grid-connected photovoltaic modules in solar power plants using state-of-art object detection YOLOv5 algorithm and classical image processing techniques. We have highlighted an integral part of the fully automated system in which a drone takes flight over the solar park and shoot the videos. Videos are preprocessed and used for trained YOLOv5 model to recognize the clean and dirty panels. The process is defined for a selected site and can be implemented using a Raspberry Pi. This system processes the images taken by drones, generates a report, and sends it to the concerned department automatically via email every day so that timely maintenance can be done for the long-life and safe operation of solar arrays. The inspection timeline for the same process was about one hundred and twenty hours, reduced to five minutes. It means that 99.93% of the time is saved through vision and robust automation techniques.

关键词： Photovoltaic systems computer vision Image processing Object detection Maintenance engineering Inspection Information technology

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A Novel LOS Rate Estimation Method Based on Images for Strap-down Inertial Guidance 3

A Novel LOS Rate Estimation Method Based on Images for Strap...

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2020 3rd International Conference on Advanced Algorithms and Control Engineering, ICAACE 2020

作者： Xu, Zheng Luo, Haibo Hui, Bin Chang, Zheng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science Shenyang110016 China Key Lab of Image Understanding and Computer Vision Shenyang110016 China

With the development of technology, precision guided weapon is becoming more and more important in modern war. In order to launch our recent guidance system on medium and small guided weapons, we propose a method to obtain the LOS rate by combining information from both camera and gyroscope. To be specific, we firstly calculate the body LOS angle through transforming the image pixel coordinate system into the image physical coordinate system according to camera internal parameters;then subtract the missile motion information contained in the measurement signal of the seeker and finally the LOS rate is deduced. Comparing with traditional gimballed seekers, our strap-down seekers with camera have significantly reduced costs and influences caused by external environments on platform. © Published under licence by IOP Publishing Ltd.

关键词： Cameras

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DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation

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Informatics in Medicine Unlocked 2022年 28卷

作者： Hasan, Md. Kamrul Elahi, Md. Toufick E. Alam, Md. Ashraful Jawad, Md. Tasnim Martí, Robert Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh Computer Vision and Robotics Institute University of Girona Spain

Background and Objective: Although automated Skin Lesion Classification (SLC) is a crucial integral step in computer-aided diagnosis, it remains challenging due to variability in textures, colors, indistinguishable boundaries, and shapes. Methods: This article proposes an automated dermoscopic SLC framework named Dermoscopic Expert (DermoExpert). It combines the pre-processing and hybrid Convolutional Neural Network (hybrid-CNN). The proposed hybrid-CNN has three distinct feature extractor modules, which are fused to achieve better-depth feature maps of the lesion. Those single and fused feature maps are classified using different fully connected layers, then ensembled to predict a lesion class. In the proposed pre-processing, we apply lesion segmentation, augmentation (geometry- and intensity-based), and class rebalancing (penalizing the majority class's loss and merging additional images to the minority classes). Moreover, we leverage transfer learning from the pre-trained models. Finally, we deploy the weights of our DermoExpert to a possible web application. Results: We evaluate our DermoExpert on the ISIC-2016, ISIC-2017, and ISIC-2018 datasets, where the DermoExpert has achieved the area under the receiver operating characteristic curve (AUC) of 0.96, 0.95, and 0.97, respectively. The experimental results improve the state-of-the-art by the margins of 10.0% and 2.0%, respectively, for the ISIC-2016 and ISIC-2017 datasets in terms of AUC. The DermoExpert also outperforms by 3.0% for the ISIC-2018 dataset concerning a balanced accuracy. Conclusion: Since DermoExpert provides better classification outcomes on three different datasets, leading to a better recognition tool to assist dermatologists. Our source code and segmented masks for the ISIC-2018 dataset will be available as a public benchmark for future improvements. © 2022 The Authors

关键词： Convolutional neural networks Image augmentation ISIC skin lesion datasets Skin lesion classification and segmentation Transfer learning

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I-AUV Docking and Intervention in a Subsea Panel

I-AUV Docking and Intervention in a Subsea Panel

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Narcis Palomeras Antonio Penalver Miquel Massot-Campos Guillem Vallicrosa Pep Lluis Negre J. Javier Fernandez Pere Ridao Pedro J. Sanz Gabriel Oliver-Codina Albert Palomer Universitat de Girona Computer Vision and Robotics Institute Centre d'Investigacio en Robotica Submarina (CIRS) 17071 Girona (Spain) Universitat Jaume I Interactive and Robotic Systems Laboratory (IRSLab) 12071 Castello (Spain) Universitat de les Illes Balears Systems Robotics and Vision Group 07122 Palma de Mallorca (Spain)

ISBN: (纸本)9781479969357

While commercially available autonomous underwater vehicles (AUVs) are routinely used in survey missions, a new set of applications exist which clearly demand intervention capabilities: the maintenance of permanent underwater structures as well as the recovery of benthic stations or black-boxes are a few of them. These tasks are addressed nowadays using manned submersibles or work-class remotely operated vehicles (ROVs), equipped with teleoperated arms under human supervision. In the context of the TRITON Spanish funded project, a subsea panel docking and an intervention procedure are proposed. The light-weight intervention AUV (I-AUV) Girona 500 is used to autonomously dock into a subsea panel using a funnel-based docking method for passive accommodation. Once docked, an autonomous fixed-based manipulation system, which uses feedback from a digital camera, is used to turn a valve and plug/unplug a connector. The paper presents the techniques used for the autonomous docking and manipulation as well as how the adapted subsea panel has been designed to facilitate such operations.

关键词： Subsea Intervention Manned submersibles Underwater structures Device Panel Berthing Unmanned vehicles Digital cameras Autonomous underwater vehicles BLACK-BOX

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Activation Template Matching Loss for Explainable Face Recognition

arXiv

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arXiv 2022年

作者： Lin, Huawei Liu, Haozhe Li, Qiufu Shen, Linlin Computer Vision Institute Shenzhen University Shenzhen 518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLoss can easily learn the facial part-based representation on the target convolutional layer, where an individual channel can detect a certain face part. Our experiments on dozens of datasets show that ECLoss achieves superior explainability metrics, and at the same time improves the performance of face verification without face alignment. In addition, our visualization results also illustrate the effectiveness of the proposed ECLoss. © 2022, CC BY.

关键词： Face recognition

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Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

arXiv

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arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Araujo, Helder Konukoglu, Ender Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Engineering Department Bogazici Univesity Turkey Institute for Systems and Robotics Universidade de Coimbra Portugal Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. Copyright © 2017, The Authors. All rights reserved.

关键词： vision

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