LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 8 of total 8

Search options

  1. Article ; Online: SPS: Accurate and Real-Time Semantic Positioning System Based on Low-Cost DEM Maps.

    Cai, Jun-Xiong / Feng, Wensen / Chen, Hao-Xiang / Mu, Tai-Jiang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

    2023  Volume 32, Page(s) 6401–6412

    Abstract: This paper presents a Semantic Positioning System (SPS) to enhance the accuracy of mobile device geo-localization in outdoor urban environments. Although the traditional Global Positioning System (GPS) can offer a rough localization, it lacks the ... ...

    Abstract This paper presents a Semantic Positioning System (SPS) to enhance the accuracy of mobile device geo-localization in outdoor urban environments. Although the traditional Global Positioning System (GPS) can offer a rough localization, it lacks the necessary accuracy for applications such as Augmented Reality (AR). Our SPS integrates Geographic Information System (GIS) data, GPS signals, and visual image information to estimate the 6 Degree-of-Freedom (DoF) pose through cross-view semantic matching. This approach has excellent scalability to support GIS context with Levels of Detail (LOD). The map data representation is Digital Elevation Model (DEM), a cost-effective aerial map that allows for fast deployment for large-scale areas. However, the DEM lacks geometric and texture details, making it challenging for traditional visual feature extraction to establish pixel/voxel level cross-view correspondences. To address this, we sample observation pixels from the query ground-view image using predicted semantic labels. We then propose an iterative homography estimation method with semantic correspondences. To improve the efficiency of the overall system, we further employ a heuristic search to speedup the matching process. The proposed method is robust, real-time, and automatic. Quantitative experiments on the challenging Bund dataset show that we achieve a positioning accuracy of 73.24%, surpassing the baseline skyline-based method by 20%. Compared with the state-of-the-art semantic-based approach on the Kitti dataset, we improve the positioning accuracy by an average of 5%.
    Language English
    Publishing date 2023-11-28
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0042
    ISSN (online) 1941-0042
    DOI 10.1109/TIP.2023.3332212
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Article ; Online: Semantic Labeling and Instance Segmentation of 3D Point Clouds Using Patch Context Analysis and Multiscale Processing.

    Hu, Shi-Min / Cai, Jun-Xiong / Lai, Yu-Kun

    IEEE transactions on visualization and computer graphics

    2018  Volume 26, Issue 7, Page(s) 2485–2498

    Abstract: We present a novel algorithm for semantic segmentation and labeling of 3D point clouds of indoor scenes, where objects in point clouds can have significant variations and complex configurations. Effective segmentation methods decomposing point clouds ... ...

    Abstract We present a novel algorithm for semantic segmentation and labeling of 3D point clouds of indoor scenes, where objects in point clouds can have significant variations and complex configurations. Effective segmentation methods decomposing point clouds into semantically meaningful pieces are highly desirable for object recognition, scene understanding, scene modeling, etc. However, existing segmentation methods based on low-level geometry tend to either under-segment or over-segment point clouds. Our method takes a fundamentally different approach, where semantic segmentation is achieved along with labeling. To cope with substantial shape variation for objects in the same category, we first segment point clouds into surface patches and use unsupervised clustering to group patches in the training set into clusters, providing an intermediate representation for effectively learning patch relationships. During testing, we propose a novel patch segmentation and classification framework with multiscale processing, where the local segmentation level is automatically determined by exploiting the learned cluster based contextual information. Our method thus produces robust patch segmentation and semantic labeling results, avoiding parameter sensitivity. We further learn object-cluster relationships from the training set, and produce semantically meaningful object level segmentation. Our method outperforms state-of-the-art methods on several representative point cloud datasets, including S3DIS, SceneNN, Cornell RGB-D and ETH.
    Language English
    Publishing date 2018-12-27
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2018.2889944
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Book ; Online: ClusterGNN

    Shi, Yan / Cai, Jun-Xiong / Shavit, Yoli / Mu, Tai-Jiang / Feng, Wensen / Zhang, Kai

    Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching

    2022  

    Abstract: Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a quadratic complexity in the number of features. Motivated by a prior ... ...

    Abstract Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a quadratic complexity in the number of features. Motivated by a prior observation that self- and cross- attention matrices converge to a sparse representation, we propose ClusterGNN, an attentional GNN architecture which operates on clusters for learning the feature matching task. Using a progressive clustering module we adaptively divide keypoints into different subgraphs to reduce redundant connectivity, and employ a coarse-to-fine paradigm for mitigating miss-classification within images. Our approach yields a 59.7% reduction in runtime and 58.4% reduction in memory consumption for dense detection, compared to current state-of-the-art GNN-based matching, while achieving a competitive performance on various computer vision tasks.

    Comment: Have been accepted by IEEE Conference on Computer Vision and Pattern Recognition 2022
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004 ; 006
    Publishing date 2022-04-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Article ; Online: Temporally Coherent Video Harmonization Using Adversarial Networks.

    Huang, Hao-Zhi / Xu, Sen-Zhe / Cai, Jun-Xiong / Liu, Wei / Hu, Shi-Min

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

    2019  Volume 29, Page(s) 214–224

    Abstract: Compositing is one of the most important editing operations for images and videos. The process of improving the realism of composite results is often called harmonization. Previous approaches for harmonization mainly focus on images. In this paper, we ... ...

    Abstract Compositing is one of the most important editing operations for images and videos. The process of improving the realism of composite results is often called harmonization. Previous approaches for harmonization mainly focus on images. In this paper, we take one step further to attack the problem of video harmonization. Specifically, we train a convolutional neural network in an adversarial way, exploiting a pixel-wise disharmony discriminator to achieve more realistic harmonized results and introducing a temporal loss to increase temporal consistency between consecutive harmonized frames. Thanks to the pixel-wise disharmony discriminator, we are also able to relieve the need of input foreground masks. Since existing video datasets which have ground-truth foreground masks and optical flows are not sufficiently large, we propose a simple yet efficient method to build up a synthetic dataset supporting supervised training of the proposed adversarial network. The experiments show that training on our synthetic dataset generalizes well to the real-world composite dataset. In addition, our method successfully incorporates temporal consistency during training and achieves more harmonious visual results than previous methods.
    Language English
    Publishing date 2019-07-17
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0042
    ISSN (online) 1941-0042
    DOI 10.1109/TIP.2019.2925550
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article: Occurrence and decay of SARS-CoV-2 in community sewage drainage systems.

    Dong, Qian / Cai, Jun-Xiong / Liu, Yan-Chen / Ling, Hai-Bo / Wang, Qi / Xiang, Luo-Jing / Yang, Shao-Lin / Lu, Zheng-Sheng / Liu, Yi / Huang, Xia / Qu, Jiu-Hui

    Engineering (Beijing, China)

    2022  

    Abstract: The rapid spread of the coronavirus disease (COVID-19) pandemic in over 200 countries poses a substantial threat to human health. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, can be discharged with feces into the ... ...

    Abstract The rapid spread of the coronavirus disease (COVID-19) pandemic in over 200 countries poses a substantial threat to human health. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, can be discharged with feces into the drainage system. However, a comprehensive understanding of the occurrence, presence, and potential transmission of SARS-CoV-2 in sewers, especially in community sewers, is still lacking. This study investigated the virus occurrence by viral nucleic acid testing in vent stacks, septic tanks, and the main sewer outlets of community where confirmed patients had lived during the outbreak of the epidemic in Wuhan, China. The results indicated that the risk of long-term emission of SARS-CoV-2 to the environment via vent stacks of buildings was low after confirmed patients were hospitalized. SARS-CoV-2 were mainly detected in the liquid phase, as opposed to being detected in aerosols, and its RNA in the sewage of septic tanks could be detected for only four days after confirmed patients were hospitalized. The surveillance of SARS-CoV-2 in sewage could be a sensitive indicator for the possible presence of asymptomatic patients in the community, though the viral concentration could be diluted more than 10 times, depending on the sampling site, as indicated by the
    Language English
    Publishing date 2022-04-20
    Publishing country China
    Document type Journal Article
    ZDB-ID 2886869-9
    ISSN 2095-8099
    ISSN 2095-8099
    DOI 10.1016/j.eng.2022.03.012
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Book ; Online: Subdivision-Based Mesh Convolution Networks

    Hu, Shi-Min / Liu, Zheng-Ning / Guo, Meng-Hao / Cai, Jun-Xiong / Huang, Jiahui / Mu, Tai-Jiang / Martin, Ralph R.

    2021  

    Abstract: Convolutional neural networks (CNNs) have made great breakthroughs in 2D computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provides a hierarchical multi-resolution ...

    Abstract Convolutional neural networks (CNNs) have made great breakthroughs in 2D computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provides a hierarchical multi-resolution structure, in which each face in a closed 2-manifold triangle mesh is exactly adjacent to three faces. Motivated by these two observations, this paper presents SubdivNet, an innovative and versatile CNN framework for 3D triangle meshes with Loop subdivision sequence connectivity. Making an analogy between mesh faces and pixels in a 2D image allows us to present a mesh convolution operator to aggregate local features from nearby faces. By exploiting face neighborhoods, this convolution can support standard 2D convolutional network concepts, e.g. variable kernel size, stride, and dilation. Based on the multi-resolution hierarchy, we make use of pooling layers which uniformly merge four faces into one and an upsampling method which splits one face into four. Thereby, many popular 2D CNN architectures can be easily adapted to process 3D meshes. Meshes with arbitrary connectivity can be remeshed to have Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach. Extensive evaluation and various applications demonstrate SubdivNet's effectiveness and efficiency.

    Comment: Codes are available in https://github.com/lzhengning/SubdivNet
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Graphics ; Computer Science - Machine Learning ; I.3.5
    Subject code 006
    Publishing date 2021-06-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Book ; Online: Pct

    Guo, Meng-Hao / Cai, Jun-Xiong / Liu, Zheng-Ning / Mu, Tai-Jiang / Martin, Ralph R. / Hu, Shi-Min

    Point cloud transformer

    2020  

    Abstract: The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which ...

    Abstract The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation and normal estimation tasks.

    Comment: 11 pages, 5 figures
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Publishing date 2020-12-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  8. Article: [Innovative ET cover system and its hydrologic evaluation].

    Liu, Chuan-shun / Cai, Jun-xiong / Wang, Jing-zhai / Rong, Yu

    Huan jing ke xue= Huanjing kexue

    2010  Volume 31, Issue 7, Page(s) 1695–1700

    Abstract: The evapotranspiration (ET) cover system,as an alternative cover system of landfill, has been used in many remediation projects since 2003. It is an inexpensive, practical,and easily maintained biological system, but is mainly favorable in arid and ... ...

    Abstract The evapotranspiration (ET) cover system,as an alternative cover system of landfill, has been used in many remediation projects since 2003. It is an inexpensive, practical,and easily maintained biological system, but is mainly favorable in arid and semiarid sites due to limited water-holding capacity of the single loam layer and limited transpiration of grass. To improve the effectiveness of percolation control, an innovative scheme of ET was suggested in this paper: (1) a clay liner was added under the single loam layer to increase the water-holding capacity; (2) combined vegetation consisting of shrub and grass was used to replace the grass cover. Hydrologic evaluation of conventional cover,ET cover and the innovative ET cover under the same condition was performed using the computer program HELP, which showed the performance of the innovative ET cover is obviously superior to that of ET cover and conventional cover.
    MeSH term(s) Biodegradation, Environmental ; Models, Theoretical ; Plant Development ; Plant Transpiration ; Plants/metabolism ; Refuse Disposal/methods ; Soil/analysis ; Water Movements ; Water Pollution/analysis
    Chemical Substances Soil
    Language Chinese
    Publishing date 2010-07
    Publishing country China
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 0250-3301
    ISSN 0250-3301
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top