检索结果-内蒙古大学图书馆

Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration

学校读者我要写书评

暂无评论

Benchmarking Classical and Learning-Based Multibeam Point Cl...

IEEE International Conference on robotics and Automation (ICRA)

作者： Li Ling Jun Zhang Nils Bore John Folkesson Anna Wåhlin Division of Robotics Perception and Learning (RPL) KTH Royal Institute of Technology Stockholm Sweden Institute of Computer Graphics and Vision (ICGV) TU Graz Austria Ocean Infinity Västra Frölunda Sweden Department of Marine Sciences University of Gothenburg Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family. In this work, we curate and release DotsonEast Dataset, a semi-synthetic MBES registration dataset constructed from an autonomous underwater vehicle in West Antarctica. Using this dataset, we systematically benchmark the performance of 2 classical and 4 learning-based methods. The experimental results show that the learning-based methods work well for coarse alignment, and are better at recovering rough transforms consistently at high overlap (20-50%). In comparison, GICP (a variant of ICP) performs well for fine alignment and is better across all metrics at extremely low overlap (10%). To the best of our knowledge, this is the first work to benchmark both learning-based and classical registration methods on an AUV-based MBES dataset. To facilitate future research, both the code and data are made available online. 1

关键词： Point cloud compression Learning systems Measurement Three-dimensional displays Simultaneous localization and mapping Iterative closest point algorithm Transforms

Fast Candidate Region Extraction for SAR Ship Target 37

学校读者我要写书评

暂无评论

Fast Candidate Region Extraction for SAR Ship Target

37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

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

ISBN: (纸本)9781665465366

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset. © 2022 IEEE.

关键词： Synthetic aperture radar

CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Dhiaf, Marwa Souibgui, Mohamed Ali Wang, Kai Liu, Yuyang Kessentini, Yousri Fornés, Alicia Rouhou, Ahmed Cheikh InstaDeep United Kingdom Computer Vision Center UAB Spain Digital Research Center of Sfax SM@RTS Tunisia State Key Laboratory of Robotics China Shenyang Institute of Automation Chinese Academy of Sciences China

Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, i.e. when data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance in English, Italian, and Russian, whilst adding only a few parameters per task. The code and trained models will be publicly available1 © 2023, CC BY.

关键词： Supervised learning

Pointsurface: Discriminative Point Cloud Surface Feature Extraction for 3d Face Recognition

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Yang, Junpeng Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Guangdong Shenzhen518172 China

3D face recognition performs better than 2D face recognition, in terms of robustness against lighting and digital attacks. Compared to 2D data, the rich geometric information in 3D data could be very useful to improve the performance of 3D face recognition. This paper introduces PointSurFace, a surface feature extraction integrated model tailored for point cloud based 3D face recognition. PointSurFace adopts a specially designed surface feature extraction module, which explores information about the position and normals of the 3D face surface to explicitly extract local discriminative features from 3D point cloud faces. Subsequently, it alternates between set abstraction (SA) layers and Inverted Residual MLP (InvResMLP) modules to effectively extract features from the point cloud data. To better handle the spatial relationships in the point cloud data, PointSurFace integrates coordinate and feature data within each SA layer and InvResMLP module. It also applies channel fusion operations to align these different types of data, thereby more effectively extracts point cloud features. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing previous methods by 2.02% and 3.19%. Results on cross-quality datasets also affirm PointSurFace’s strong generalization capabilities. Ablation experiments validate the effectiveness of each module within PointSurFace, which suggests that the proposed surface feature extraction module significantly enhances the point cloud based 3D facial recognition performance. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly

学校读者我要写书评

暂无评论

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

Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Xing, Hao Burschka, Darius Technical University of Munich Machine Vision and Perception Group Munich Institute of Robotics and Machine Intelligence Department of Computer Science Parkring 13 Munich85748 Germany

In skeleton-based action recognition, Graph Convolutional Networks model human skeletal joints as vertices and connect them through an adjacency matrix, which can be seen as a local attention mask. However, in most existing Graph Convolutional Networks, the local attention mask is defined based on natural connections of human skeleton joints and ignores the dynamic relations for example between head, hands and feet joints. In addition, the attention mechanism has been proven effective in Natural Language Processing and image description, which is rarely investigated in existing methods. In this work, we proposed a new adaptive spatial attention layer that extends local attention map to global based on relative distance and relative angle information. Moreover, we design a new initial graph adjacency matrix that connects head, hands and feet, which shows visible improvement in terms of action recognition accuracy. The proposed model is evaluated on two large-scale and challenging datasets in the field of human activities in daily life: NTU-RGB+D and Kinetics skeleton. The results demonstrate that our model has strong performance on both dataset. © 2022, CC BY.

关键词： Convolution

2mm Diameter Continuum Robot Tools for Suturing in Open Spina Bifida Repair

学校读者我要写书评

暂无评论

2mm Diameter Continuum Robot Tools for Suturing in Open Spin...

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

作者： Arion Law Nillan Nimal Paul H. Kang Radian Gondokaryono James Drake Tim Van Mieghem Thomas Looi Department of Mechanical and Industrial Engineering University of Toronto Canada Wilfred and Joyce Posluns Centre for Image Guided Innovation and Therapeutic Intervention Hospital for Sick Children Toronto Canada Institute of Biomedical Engineering University of Toronto Canada Department of Computer Science and the Medical Computer Vision and Robotics Lab University of Toronto Canada Mount Sinai Hospital Toronto Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Open Spina Bifida (OSB) is a congenital neural tube defect where a major component of the procedure to repair the defect involves the closure of a lesion wound through suturing. For a minimally invasive approach, tools entering the uterus to access the fetus should be as thin as possible to minimize maternal risk. This work presents the design of a 3 degrees-of-freedom, 2mm diameter tool wrist with a bending range of motion from 0° to 90°. This wrist is capable of generating up to 2N of force measured from the end of the wrist and achieving a bending curvature of 107m -1 (9.35mm bending radius). A pseudo-rigid body kinematic model has been implemented for the control of this tool with a protocol for singularity mitigation and avoidance. Timed teleoperation studies explicitly demonstrate that the tool is able to reliably execute suturing with a fastest achieved time of under 3 minutes for a simple interrupted suturing technique.

关键词： Wrist Protocols Prevention and mitigation Uterus Force Bending Maintenance engineering Wounds Reliability engineering Motion control

FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Elbatel, Marawan Martí, Robert Li, Xiaomeng The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Computer Vision and Robotics Institute University of Girona Spain The Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases. However, existing methods often overlook the frequency-dependent behavior of these models, thereby limiting their effectiveness in transferring representations and generalizations to rare diseases. In this paper, we propose FoPro-KD, a novel framework that leverages the power of frequency patterns learned from frozen pretrained models to enhance their transferability and compression, presenting a few unique insights: 1) We demonstrate that leveraging representations from publicly available pre-trained models can substantially improve performance, specifically for rare classes, even when utilizing representations from a smaller pre-trained model. 2) We observe that pre-trained models exhibit frequency preferences, which we explore using our proposed Fourier Prompt Generator (FPG), allowing us to manipulate specific frequencies in the input image, enhancing the discriminative representational transfer. 3) By amplifying or diminishing these frequencies in the input image, we enable Effective Knowledge Distillation (EKD). EKD facilitates the transfer of knowledge from pre-trained models to smaller models. Through extensive experiments in long-tailed gastrointestinal image recognition and skin lesion classification, where rare diseases are prevalent, our FoPro-KD framework outperforms existing methods, enabling more accessible medical models for rare disease classification. Code is available at https://***/xmed-lab/FoPro-KD. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

Towards a Unified Approach to Homography Estimation Using Image Features and Pixel Intensities

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Nogueira, Lucas de Paiva, Ely C. Silveira, Geraldo School of Mechanical Engineering University of Campinas Campinas SP Brazil Robotics and Computer Vision Research Group Center for Information Technology Renato Archer Campinas SP Brazil

The homography matrix is a key component in various vision-based robotic tasks. Traditionally, homography estimation algorithms are classified into feature- or intensity-based. The main advantages of the latter are their versatility, accuracy, and robustness to arbitrary illumination changes. On the other hand, they have a smaller domain of convergence than the feature-based solutions. Their combination is hence promising, but existing techniques only apply them sequentially. This paper proposes a new hybrid method that unifies both classes into a single nonlinear optimization procedure, applies the same minimization method, and uses the same homography parametrization and warping function. Experimental validation using a classical testing framework shows that the proposed unified approach has improved convergence properties compared to each individual class. These are also demonstrated in a visual tracking application. As a final contribution, our ready-to-use implementation of the algorithm is made publicly available to the research community. © 2022, CC BY.

关键词： computer vision