检索结果-内蒙古大学图书馆

22nd International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Bai, Xiaolan Huang, May Prasad, Neeli Rashmi Mihovska, Albena Dimitrova Int Technol Univ Elect & Comp Engn Departm Santa Clara CA 95050 USA Aarhus Univ Aarhus BSS Herring Denmark

ISBN: (纸本)9781728154190

The development of deep learning has rapidly updated image-based localization techniques. This paper presents a review and comparison of the current state-of-the-art methods for image-based localization using deep learning in the indoor environment. Traditional Global Structure from Motion (SfM) pipeline and learning-based pipeline from the recent techniques have been analyzed. based on the pipeline, the methods are categorized into three groups: learned features and matching, learned relative pose estimation, and learned absolute pose estimation. Since multiple sensors are used in many applications, sensor fusion techniques including image information, have been briefly reviewed in this paper as well. Furthermore, the paper discusses challenges in these methods and concludes learned features and matching is the more competitive method for indoor localization.

关键词： image-based localization indoor localization deep learning CNN SfM

来源：评论

学校读者我要写书评

暂无评论

image based localization with Simulated Egocentric Navigations 14

Image Based Localization with Simulated Egocentric Navigatio...

引用

14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)

作者： Orlando, Santi A. Furnari, Antonino Battiato, Sebastiano Farinella, Giovanni M. Univ Catania Dept Math & Comp Sci Catania Italy DWORD Xenia Progetti Srl Catania Italy

ISBN: (纸本)9789897583544

Current methods for image-based localization (IBL) require the collection and labelling of large datasets of images or videos for training purpose. Such procedure generally requires a significant effort, involving the use of dedicated sensors or the employment of structure from motion techniques. To overcome the difficulties of acquiring a dataset suitable to train models to study IBL, we propose a tool to generate simulated egocentric data starting from 3D models of real indoor environments. The generated data are automatically associated to the 3D camera pose information to be exploited during training, hence avoiding the need to perform "manual" labelling. To asses the effectiveness of the proposed tool, we generate and release a huge dataset of egocentric images using a 3D model of a real environment belonging to the S3DIS dataset. We also perform a benchmark study for 3 Degrees of Freedom (3DoF) indoor localization by considering an IBL pipeline based on image-retrieval and a triplet network. Results highlight that the generated dataset is useful to study the IBL problem and allows to perform experiments with different settings in a simple way.

关键词： image-based localization Similarity Search Metric Learning Simulated Data

来源：评论

学校读者我要写书评

暂无评论

image-based localization Using Deep Neural Networks

Image-Based Localization Using Deep Neural Networks

引用

作者： Xiaotian Li Aalto University

学位级别：硕士

image-based localization, or camera relocalization, is a fundamental problem in computer vision and robotics, and it refers to estimating camera pose from an image. It is a key component of many computer vision applications such as navigating autonomous vehicles and mobile robotics, simultaneous localization and mapping (SLAM), and augmented reality. Currently, there are plenty of image-based localization methods proposed in the literature. Most state-of-the-art approaches are based on hand-crafted local features, such as SIFT, ORB, or SURF, and efficient 2D-to-3D matching using a 3D model. However, the limitations of the hand-crafted feature detector and descriptor become the bottleneck of these approaches. Recently, some promising deep neural network based localization approaches have been proposed. These approaches directly formulate 6 DoF pose estimation as a regression problem or use neural networks for generating 2D-3D correspondences, and thus no feature extraction or feature matching processes are required. In this thesis, we first review two state-of-the-art approaches for image-based localization. The first approach is conventional hand-crafted local feature based (Active Search) and the second one is novel deep neural network based (DSAC). Building on the idea of DSAC, we then examine the use of conventional RANSAC and introduce a novel full-frame Coordinate CNN. We evaluate these methods on the 7-Scenes dataset of Microsoft Research, and extensive comparisons are made. The results show that our modifications to the original DSAC pipeline lead to better performance than the two state-of-the-art approaches.

关键词： computer vision machine learning deep neural networks image-based localization

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Point Cloud Simplification Framework for City-Scale image-based localization

引用

IEEE TRANSACTIONS ON image PROCESSING 2017年第1期26卷 262-275页

作者： Cheng, Wentao Lin, Weisi Zhang, Xinfeng Goesele, Michael Sun, Ming-Ting Nanyang Technol Univ Fraunhofer IDM Res Ctr Singapore 639798 Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Nanyang Technol Univ Rapid Rich Object Search Lab Singapore 639798 Singapore Tech Univ Darmstadt Dept Comp Sci D-64283 Darmstadt Germany Univ Washington Dept Elect Engn Seattle WA 98105 USA

City-scale 3D point clouds reconstructed via structure-from-motion from a large collection of Internet images are widely used in the image-based localization task to estimate a 6-DOF camera pose of a query image. Due to prohibitive memory footprint of city-scale point clouds, image-based localization is difficult to be implemented on devices with limited memory resources. Point cloud simplification aims to select a subset of points to achieve a comparable localization performance using the original point cloud. In this paper, we propose a data-driven point cloud simplification framework by taking it as a weighted K-Cover problem, which mainly includes two complementary parts. First, a utility-based parameter determination method is proposed to select a reasonable parameter K for K-Cover-based approaches by evaluating the potential of a point cloud for establishing sufficient 2D-3D feature correspondences. Second, we formulate the 3D point cloud simplification problem as a weighted K-Cover problem, and propose an adaptive exponential weight function based on the visibility probability of 3D points. The experimental results on three popular datasets demonstrate that the proposed point cloud simplification framework outperforms the state-of-the-art methods for the image-based localization application with a well predicted parameter in the K-Cover problem.

关键词： Point cloud simplification image-based localization visibility probability

来源：评论

学校读者我要写书评

暂无评论

Efficient & Effective Prioritized Matching for Large-Scale image-based localization

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017年第9期39卷 1744-1756页

作者： Sattler, Torsten Leibe, Bastian Kobbelt, Leif Swiss Fed Inst Technol Dept Comp Sci Comp Vis Grp Ramistr CH-8092 Zurich Switzerland Rhein Westfal TH Aachen Comp Vis Grp D-52062 Aachen Germany Rhein Westfal TH Aachen Comp Graph Grp D-52062 Aachen Germany

Accurately determining the position and orientation from which an image was taken, i.e., computing the camera pose, is a fundamental step in many Computer Vision applications. The pose can be recovered from 2D-3D matches between 2D image positions and points in a 3D model of the scene. Recent advances in Structure-from-Motion allow us to reconstruct large scenes and thus create the need for image-based localization methods that efficiently handle large-scale 3D models while still being effective, i.e., while localizing as many images as possible. This paper presents an approach for large scale image-based localization that is both efficient and effective. At the core of our approach is a novel prioritized matching step that enables us to first consider features more likely to yield 2D-to-3D matches and to terminate the correspondence search as soon as enough matches have been found. Matches initially lost due to quantization are efficiently recovered by integrating 3D-to-2D search. We show how visibility information from the reconstruction process can be used to improve the efficiency of our approach. We evaluate the performance of our method through extensive experiments and demonstrate that it offers the best combination of efficiency and effectiveness among current state-of-the-art approaches for localization.

关键词： image-based localization location recognition prioritized feature matching camera pose estimation

来源：评论

学校读者我要写书评

暂无评论

Resource-efficient and Automated image-based Indoor localization

引用

ACM TRANSACTIONS ON SENSOR NETWORKS 2019年第2期15卷 19-19页

作者： Niu, Qun Li, Mingkuan He, Suining Gao, Chengying Chan, S. -H. Gary Luo, Xiaonan Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Technol Guilin 541004 Peoples R China

image-based indoor localization has aroused much interest recently because it requires no infrastructure support. Previous approaches on image-based localization, due to their computation and storage requirements, often process queries at servers. This does not scale well, incurs round-trip delay, and requires constant network connectivity. Many also require users to manually confirm the shortlisted matched landmarks, which is inconvenient, slow, and prone to selection error. To overcome these limitations, we propose a highly automated (in terms of image confirmation after taking images) image-based localization algorithm (HAIL), distributed in mobile devices. HAIL achieves resource efficiency (in terms of storage and processing) by keeping only distinguishing visual features for each landmark, and employing the efficient k-d tree to search for features. It further utilizes motion sensors and map constraints to enhance the localization accuracy without user operation. We have implemented HAIL on Android platforms and conducted extensive experiments in a food plaza and a premium shopping mall. Experimental results show that it achieves much higher localization accuracy (reducing the localization error by more than 20%) and computation efficiency (by more than 40% in time) as compared with the state-of-the-art approaches.

关键词： image-based localization feature selection automated image selection joint constraints smartphone-based localization

来源：评论

学校读者我要写书评

暂无评论

3D image-based Indoor localization Joint With WiFi Positioning 18

3D Image-based Indoor Localization Joint With WiFi Positioni...

引用

8th ACM International Conference on Multimedia Retrieval (ACM ICMR)

作者： Lu, Guoyu Song, Jingkuan Rochester Inst Technol Chester F Calson Ctr Imaging Sci Rochester NY 14623 USA Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781450350464

We realize a system that utilizes WiFi to facilitate the image-based localization system, which avoids the confusion caused by the similar decoration inside the buildings. While WiFi-based localization thread obtains the rough location information, the image-based localization thread retrieves the best matching images and clusters the camera poses associated with the images into different location candidates. The image cluster closest to the WiFi localization outcome is selected for the exact camera pose estimation. The usage of WiFi significantly reduces the search scope, avoiding the extensive search of millions of descriptors in a 3D model. In the image-based localization stage, we also propose a novel 2D-to2D-to-3D localization framework which follows a coarse-to-fine strategy to quickly locate the query image in several location candidates and performs the local feature matching and camera pose estimation after choosing the correct image location by WiFi positioning. The entire system demonstrates significant benefits in combining both images and WiFi signals in localization tasks and great potential to be deployed in real applications.

关键词： image-based localization WiFi positioning 3D registration image retrieval Camera pose estimation

来源：评论

学校读者我要写书评

暂无评论

Particle Filter based Tracking with image-based localization 20th

Particle Filter Based Tracking with Image-Based Localization

引用

20th International Conference on Knowledge - based and Intelligent Information and Engineering Systems (KES)

作者： Fukui, Shinji Hayakawa, Sou Iwahori, Yuji Nakamura, Tsuyoshi Bhuyan, M. K. Aichi Univ Educ Hirosawa 1 Kariya Aichi 4488542 Japan Chubu Univ Matsumoto Cho 1200 Kasugai Aichi 4878501 Japan Nagoya Inst Technol Showa Ku Gokiso Cho Nagoya Aichi 4668555 Japan Indian Inst Technol Gauhati 781039 Guwahati India

In this paper, we propose a new method for object tracking robust to the intersection with other objects with similar appearance and to the great rotation of the camera. The method uses 3D information of the feature points and the camera position by the image-based localization method. The movement information of the camera is used by the prediction process and the calculation process of the likelihood. Furthermore, the method extracts foreground objects by the homography transformation. The result is used by the likelihood function and by the process judging whether the target object is occluded by a background object or not. The proposed method can track the target object robustly when the camera rotates greatly and when the target object is occluded by the background object or the other object. Results are demonstrated by experiments using real videos. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of KES International

关键词： Tracking Particle Filter image-based localization Homography Transformation

来源：评论

学校读者我要写书评

暂无评论

image-based localization using image Database and Local 3D Maps 12

Image-based Localization using Image Database and Local 3D M...

引用

12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Kim, Hyungjin Jung, Kwangyik Oh, Taekjun Myung, Hyun Korea Adv Inst Sci & Technol URL 291 Daehak Ro 373-1 Guseong Dong Daejeon 34141 South Korea

ISBN: (纸本)9781467379717

image-based localization is one of the most important techniques for autonomous navigation. This paper proposes a 6-DoF localization method based on image database and local 3D maps. To perform localization, the image database and local 3D map of each image are collected in advance. By applying a fast place recognition algorithm such as FAB-MAP, an initial position is assumed to be available at a past position of a matched image from the database. Then, a 6-DoF camera pose is estimated using PnP algorithm by 2D-to-3D matching correspondences in a query image and a local 3D map. The proposed algorithm is demonstrated in real environment.

关键词： image-based localization 2D-to-3D matching correspondences FAB-MAP camera pose estimation

来源：评论

学校读者我要写书评

暂无评论

Particle Filter based Tracking with image-based localization

引用

Procedia Computer Science 2016年 96卷 977-986页

作者： Shinji Fukui Sou Hayakawa Yuji Iwahori Tsuyoshi Nakamura M.K. Bhuyan Aichi University of Education Hirosawa 1 Igaya Kariya 448-8542 Japan Chubu University Matsumoto-cho 1200 Kasugai 487-8501 Japan Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya 466-8555 Japan Indian Institute of Technology Guwahati Guwahati - 781039 India

关键词： Tracking Particle Filter image-based localization Homography Transformation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：