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Alignment of Point Clouds for Comparison of Infrastructures in Civil engineering on Quality Control in Metrology 27

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Alignment of Point Clouds for Comparison of Infrastructures ...

27th International Conference in Central Europe on computer Graphics, Visualization and computer vision, WSCG 2019

作者： Haddad, Hadi Khaksari Laurendeau, Denis Computer Vision and Systems lab Department of Electrical and Computer Engineering Laval University Quebec city Canada

ISBN: (纸本)9788086943374

For 3D point cloud registration, Go-ICP [Yang et al., 2016] has been shown to obtain the global optimal solution for a pair composed of a model point cloud and a data point cloud. Go-ICP mostly has been investigated only on standard sets of point clouds. In this paper, we demonstrate the remarkable efficacy of Go-ICP for the alignment of very complex large-scale point clouds to their corresponding deformed CAD models. In particular, given two distinct sets of point clouds taken from the exterior and the interior of a building, experiments demonstrate that Go-ICP is able to successfully align both of these sets to the point cloud of the CAD model of the whole building (both exterior and interior information included). With the experimentation presented in this paper, we demonstrate that Go-ICP can achieve excellent alignment results and that this approach can be deployed in applications aiming at comparing CAD models of a building ("as designed" model) to the point cloud of the actual building ("as-built" model). Experiments also demonstrate the efficacy of Go-ICP to align a deformed copy of a man-made object to the original object in quality control applications. © 2019 University of West Bohemia. All rights reserved.

关键词： Alignment

Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

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Research 2024年第1期6卷 707-720页

作者： Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China

Data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising *** this paper,we proposed an untargeted analysis method,Dear-DIA^(XMBD),for direct analysis of DIA ***-DIA^(XMBD) first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms,then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes,and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and *** show that Dear-DIA^(XMBD) performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms.

关键词： Deep Auto Independent

Collaborative spatial-temporal modeling for language-queried video actor segmentation

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arXiv 2021年

作者： Hui, Tianrui Huang, Shaofei Liu, Si Ding, Zihan Li, Guanbin Wang, Wenguan Han, Jizhong Wang, Fei Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Institute of Artificial Intelligence Beihang University School of Computer Science and Engineering Sun Yat-sen University SenseTime Research Computer Vision Lab Eth Zurich Pazhou Lab Guangzhou China

Language-queried video actor segmentation aims to predict the pixel-level mask of the actor which performs the actions described by a natural language query in the target frames. Existing methods adopt 3D CNNs over the video clip as a general encoder to extract a mixed spatiotemporal feature for the target frame. Though 3D convolutions are amenable to recognizing which actor is performing the queried actions, it also inevitably introduces misaligned spatial information from adjacent frames, which confuses features of the target frame and yields inaccurate segmentation. Therefore, we propose a collaborative spatial-temporal encoder-decoder framework which contains a 3D temporal encoder over the video clip to recognize the queried actions, and a 2D spatial encoder over the target frame to accurately segment the queried actors. In the decoder, a Language-Guided Feature Selection (LGFS) module is proposed to flexibly integrate spatial and temporal features from the two encoders. We also propose a Cross-Modal Adaptive Modulation (CMAM) module to dynamically recombine spatial- and temporal-relevant linguistic features for multimodal feature interaction in each stage of the two encoders. Our method achieves new stateof- the-art performance on two popular benchmarks with less computational overhead than previous approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

Tunable unidirectional surface plasmon-polaritons at the interface between gyrotropic and isotropic conductors 22

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Tunable unidirectional surface plasmon-polaritons at the int...

22nd International Conference on Electromagnetics in Advanced Applications, ICEAA 2021

作者： Liang, Y. Pakniyat, S. Xiang, Y. Chen, J. Shi, F. Hanson, G.W. Cen, C. West Virginia University Department of Physics and Astronomy MorgantownWV26506 United States University of Wisconsin-Milwaukee Department of Electrical Engineering MilwaukeeWI53211 United States University of Pittsburgh Peterson Institute of Nano Science and Engineering Department of Electrical and Computer Engineering PittsburghPA15261 United States Guangxi Key Lab for Relativistic Astrophysics Center on Nanoenergy Research School of Physical Science and Technology Guangxi University Nanning Guangxi530004 China Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781665413862

The propagation of SPPs in solid state systems is usually reciprocal, which means that a forward-traveling SPP wave can be easily backscattered by disorders. Since such backscattering is detrimental for SPP-based information processing applications, the development of nonreciprocal plasmonic platforms that enforce unidirectional SPP propagations is of great importance. © 2021 IEEE.

关键词： Electromagnetic wave polarization

A Point-to-distribution Degeneracy Detection Factor for LiDAR SLAM using Local Geometric Models

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A Point-to-distribution Degeneracy Detection Factor for LiDA...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Sehua Ji Weinan Chen Zerong Su Yisheng Guan Jiehao Li Hong Zhang Haifei Zhu Biomimetic and Intelligent Robotics Lab (BIRL) School of Electromechanical Engineer Guangdong University of Technology Guangzhou China JT-Innovation (Guangdong) Intelligent Technology Co. Ltd. Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China College of Engineering South China Agricultural University China Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://***/jisehua/***. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-SLAM systems.

关键词： Point cloud compression Accuracy Simultaneous localization and mapping Sensitivity Laser radar Geometric modeling Noise

Temporal transformer networks with self-supervision for action recognition

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arXiv 2021年

作者： Zhang, Yongkang Li, Jun Wu, Guoming Zhang, Han Shi, Zhiping Liu, Zhaoxun Wu, Zizhang Jiang, Na The Information Engineering College Capital Normal University Beijing100048 China The State Key Lab of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China The Computer Vision Perception Department of ZongMu Technology Shanghai201203 China

In recent years, 2D Convolutional Networks-based video action recognition has encouragingly gained wide popularity;However, constrained by the lack of long-range non-linear temporal relation modeling and reverse motion information modeling, the performance of existing models is, therefore, undercut seriously. To address this urgent problem, we introduce a startling Temporal Transformer Network with Self-supervision (TTSN). Our high-performance TTSN mainly consists of a temporal transformer module and a temporal sequence self-supervision module. Concisely speaking, we utilize the efficient temporal transformer module to model the nonlinear temporal dependencies among non-local frames, which significantly enhances complex motion feature representations. The temporal sequence self-supervision module we employ unprecedentedly adopts the streamlined strategy of "random batch random channel" to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model. Extensive experiments on three widely used datasets (HMDB51, UCF101, and Something-something V1) have conclusively demonstrated that our proposed TTSN is promising as it successfully achieves state-of-the-art performance for action recognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Visual object tracking with discriminative filters and siamese networks: A survey and outlook

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arXiv 2021年

作者： Javed, Sajid Danelljan, Martin Khan, Fahad Shahbaz Khan, Muhammad Haris Felsberg, Michael Matas, Jiri The EECS Department Khalifa University of Science and Technology P.O Box: 127788 Abu Dhabi United Arab Emirates The Computer Vision Lab Dept. of Information Technology and Electrical Engineering ETH Zürich Switzerland Computer Vision Department MBZUAI Abu Dhabi United Arab Emirates Computer Vision Laboratory Linköping University Sweden Center for Machine Perception Czech Technical University Prague Czech Republic

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks. First, we present the background theory of both the DCF and Siamese tracking core formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms. Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering different experimental aspects of visual tracking: datasets, evaluation metrics, performance, and speed comparisons. We finish the survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis. © 2021, CC BY-SA.

关键词： Surveys

Revisiting multi-Task learning in the deep learning era

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arXiv 2020年

作者： Vandenhende, Simon Georgoulis, Stamatios Proesmans, Marc Dai, Dengxin Gool, Luc Van Center for Processing Speech and Images Department Electrical Engineering Ku Leuven Computer Vision Lab Department Electrical Engineering Eth Zurich Center for Processing Speech and Images Ku Leuven and the Computer Vision Lab Ethz

Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: Training a separate neural network for each individual task. Many real-world problems, however, call for a multi-modal approach and, therefore, for multi-Tasking models. Multi-Task learning (MTL) aims to leverage useful information across tasks to improve the generalization capability of a model. In this survey, we provide a well-rounded view on state-of-The-Art MTL techniques within the context of deep neural networks. Our contributions concern the following. First, we consider MTL from a network architecture point-of-view. We include an extensive overview and discuss the advantages/disadvantages of recent popular MTL models. Second, we examine various optimization methods to tackle the joint learning of multiple tasks. We summarize the qualitative elements of these works and explore their commonalities and differences. Finally, we provide an extensive experimental evaluation across a variety of datasets to examine the pros and cons of different methods, including both architectural and optimization based strategies. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Manna, Suvojit Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malayasia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Technology Sydney Ultimo Australia

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification. © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)