检索结果-内蒙古大学图书馆

International Symposium on Intelligent Systems and Informatics (SISY)

作者： Panka Szalka Attila Hideg Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

This study presents an innovative method to improve the accuracy of indoor mapping by deploying and integrating a sensor that transmits two-dimensional data with a three-dimensional mesh created by another device. The iterative closest point algorithm was used to coordinate the different data sets and transform their different coordinate systems into a common indoor map. Prior to this, rigorous image cleaning and matching processes are conducted to ensure the quality of the data used. The effectiveness of this methodology has been validated through multiple tests using a Lidar scanner and a HoloLens 2 Augmented Reality device. This method not only offers potential for enhancing indoor navigation and safety protocols like emergency evacuation but also presents opportunities for advancing asset tracking and other applications.

关键词： Laser radar Accuracy iterative closest point algorithm Tracking Heuristic algorithms Transforms Robot sensing systems Real-time systems Cleaning Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Homologous Image Registration algorithm Based on Generalized Dual-Bootstrap iterative closest point algorithm

Homologous Image Registration Algorithm Based on Generalized...

引用

Intelligent Communications and Computing (ICC), International Conference on

作者： Haoyang Tang Yujie Gao Ziqiao Zhao Zuqiang Bai Dongfang Yang School of Automation Xi'an University of Posts & Telecommunications Xi'an China Unit 96941 of the People's Liberation Army Xi'an China Computer Image Processing Xi'an High tech research Institute Xi'an China

ISBN: (数字)9798331530365

ISBN: (纸本)9798331530372

Heterogeneous image registration is a critical field in computer vision, with broad applications in scene registration, image retrieval, and autonomous driving. Due to differences in sensor types and imaging conditions, heterogeneous images often exhibit significant appearance discrepancies, leading to errors when using traditional registration methods. To address this challenge, we propose the Enhanced Generalized Dual-Bootstrap iterative closest point (E-GDB-ICP) algorithm, designed to improve the accuracy and robustness of heterogeneous image registration. The proposed algorithm first utilizes SIFT descriptors and a multi-scale keypoint detector to initialize feature points, significantly increasing initial registration accuracy. The feature point selection process is then refined to further enhance the reliability of matches. Additionally, a new image registration quality evaluation function is introduced, enabling the assessment of registration outcomes without requiring ground truth data. This makes it a valuable tool for real-world applications. Experimental validation on remote sensing and architectural image datasets demonstrates the effectiveness of the proposed method, which achieves improved accuracy and robustness in heterogeneous image registration tasks.

关键词： Measurement Image registration Accuracy iterative closest point algorithm Noise Detectors Feature extraction Robustness iterative methods Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Low Cost 3D Scanner Using iterative closest point algorithm

Low Cost 3D Scanner Using Iterative Closest Point Algorithm

引用

Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Arushi Sreekumar Renuka Devi S M Hemalatha T. Bappadittya Roy Electronics and Communication Department G. Narayanamma Institute of Technology and Science Hyderabad India Department of ECE Vijaya Institute of Technology for Women Enikepadu India School of Electronics Engineering VIT-AP University Amaravati India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

3D reconstruction is widely used to create 3D representation of 2D images. 3D scanner is a device that takes scans or images of the object or environment and creates the 3D representation of it. While industrial 3D scanners use laser technology to do the scanning, the central goal of this work is to compare cost effective ways of 3D scanning and comparing their performance. In this work we use a camera to capture RGBD images then use three different techniques namely, the hardcoded meth to reconstruct the objects, SIFT algorithm and the ICP algorithm. The results demonstrate that the ICP method yields the most reliable reconstructions, evidenced by Root Mean Square Error (RMSE) values approximately 0.0022 and fitness values reaching 0.9, indicating accuracy for objects such as a Rubik's cube and an orange fruit.

关键词： point cloud compression Three-dimensional displays Costs Accuracy iterative closest point algorithm Transforms Neural radiance field Approximation algorithms Reliability Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

A Novel Radiologic Assessment of Screw Loosening Focusing on Spatial Position Change of Screws Using an iterative closest point algorithm with Stereolithography Data: Technical Note

引用

WORLD NEUROSURGERY 2019年 124卷 171-177页

作者： Tanioka, Satoru Ishida, Fujimaro Kuraishi, Keita Tanaka, Katsuhiro Shimosaka, Shinichi Suzuki, Hidenori Mizuno, Masaki Mie Chuo Med Ctr Dept Neurosurg Tsu Mie Japan Mie Univ Dept Neurosurg Grad Sch Med Tsu Mie Japan

OBJECTIVE: In thoracolumbar spinal instrumentation surgery, pedicle screw fixation is widely used, whereas screw loosening occurs only occasionally over time. It is common to evaluate screw loosening by the radiographic lucent zone around screws, which can neither evaluate loosening quantitatively nor detect slight screw loosening. In the present technical note, we describe a novel assessment technique of screw loosening by generating 3-dimensional screw images from computed tomography data and superposing them in time series. METHODS: Computed tomography data were exported in digital imaging and communications in medicine dataset and imported to the 3-dimensional computer-aided designing software, by which screws and rods were segmented and outputted in stereolithography (STL) format. The STL files were imported to the software, and registration based on iterative closest point algorithm was performed to assess screw position changes. RESULTS: Positional changes on STL in time series were classified into 3 types: 1) no position changes existed in the entire system of screws and rods;2) position changes existed in the entire system of screws and rods, but no position changes were shown when the left-sided and right-sided screws and rod were separately evaluated;and 3) position changes existed in the left and/or right-sided screws and rods even when evaluated separately. CONCLUSIONS: This technique enables the quantitative evaluation of screw loosening and loosening between screws and rods. In conjunction with conventional methods of assessing radiographic lucent zone, we are able to obtain more accurate information regarding screw loosening after spinal instrumentation surgery.

关键词： iterative closest point algorithm Screw loosening Spatial position change of screws Spinal instrumentation surgery Stereolithography

来源：评论

学校读者我要写书评

暂无评论

An Improved Semi-Dense iterative closest point Registration algorithm Fusing Intrinsic Shape Signatures and Curvature Feature

An Improved Semi-Dense Iterative Closest Point Registration ...

引用

Intelligent Computing and Robotics (ICICR), International Conference on

作者： Ye Li Rong Fei Tong Zhang Institute of Computer Science & Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798350372274

ISBN: (纸本)9798350372281

point clouds registration is a key step in three- dimension data processing and the iterative closest point algorithm has been widely for this task. An improved semi-dense iterative closest point registration algorithm fusing Intrinsic Shape Signatures and curvature feature is proposed in this paper to solve the problem of large-scale and small-overlap point clouds registration. Firstly, the semi-dense point cloud is generated by a voxel grid filter and the feature points of Intrinsic Shape Signatures are extracted from the semi-dense point cloud for coarse registration. Then in coarse registration step, the super 4-points Congruent Sets algorithm is used to get better registration results, even on partial overlapping point clouds. Finally, fine registration is completed by using an improved iterative closest point algorithm based on curvature feature and K-D tree. Experiments on indoor and outdoor 3D point cloud data show that the proposed algorithm in this paper is more accurate, efficient and robust compared with the traditional ICP algorithm, especially for large-scale and small-overlap point clouds.

关键词： point cloud compression Solid modeling Three-dimensional displays Accuracy Shape iterative closest point algorithm Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Improved iterative closest point algorithm using Truncated Signed Distance Function 18

Improved Iterative Closest Point Algorithm using Truncated S...

引用

18th International Conference on Control, Automation and Systems (ICCAS)

作者： Kim, Hanjun Hong, H. K. Lee, B. H. Seoul Natl Univ Dept Elect & Comp Engn ASRI Seoul 08826 South Korea

ISBN: (纸本)9788993215151

Truncated signed distance is one of the methods to represent 3D spatial information by using the distance from the surface of an object, and is mainly used for 3D dense reconstruction. When using truncated signed distance value for 3D reconstruction, iterative closest point algorithm is generally used for matching two point clouds. However, iterative closest point algorithm for 3D reconstruction uses only the geometric information of point clouds themselves. Due to lack of information in the surface structure, it is possible to fall in the local minimum or occur convergence failure in iteration process. To solve these problems, we use additional information from truncated signed distance function values. As we optimize the joint objective function composed of the geometric information and the truncated signed distance function information, it is possible to register two point clouds to be more robust in iterative closest point algorithm. Experimental results demonstrate that the proposed method outperforms the conventional method using only a geometric information.

关键词： iterative closest point algorithm truncated signed distance function 3D reconstruction 3D registration

来源：评论

学校读者我要写书评

暂无评论

Improved iterative closest point algorithm using Truncated Signed Distance Function

Improved Iterative Closest Point Algorithm using Truncated S...

引用

International Conference on Control, Automation and Systems

作者： Hanjun Kim H. K. Hong B. H. Lee ASRI Department of Electrical and Computer Engineering Seoul National University Seoul Korea

ISBN: (纸本)9781538670804;9788993215168

关键词： iterative closest point algorithm Truncated signed distance function 3D reconstruction 3D registration

来源：评论

学校读者我要写书评

暂无评论

Robust Scale iterative closest point algorithm Based on Correntropy for point Set Registration

Robust Scale Iterative Closest Point Algorithm Based on Corr...

引用

Australian Control Conference (AuCC)

作者： Chen, Hongchen Wu, Zongze Du, Shaoyi Zhou, Nan Sun, Jing South China Univ Technol Sch Elect & Informat Engn Guangzhou 510000 Guangdong Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781922107909

point set registration is important for calibration of multiple cameras, 3D reconstruction and recognition, etc. The traditional scale iterative closest point (ICP) algorithm is fast and accurate for scale registration of point sets, but it performs worse when the point sets with large outliers or noise. This paper introduces a novel algorithm based on correntropy for scale registration of noisy point sets. Firstly, the definition of correntropy is introduced and its property of eliminating outliers is demonstrated by comparing with the mean square error (MSE). Secondly, a novel objective function of scale registration problem is proposed by applying the maximum correntropy criterion (MCC). After that, a new scale ICP algorithm is proposed to solve this energy function. This method uses a simple iterative algorithm and computes the scale transformation by methods including half-quadratic technique and derivation quickly at each iterative step. Similar to the ICP algorithm, this new algorithm converges monotonically to a local maximum for any given initial parameters. Experimental results demonstrate that our algorithm has the high speed and accuracy for scale registration with outliers compared with the traditional scale ICP algorithm and the state-of-the-art algorithms.

关键词： iterative closest point algorithm Robustness Linear programming Kernel Random variables Optimization Australia Set of points Random variable Mean square error outliers iterative closest point algorithm Registration linear programming Inductively coupled plasma Robustness Scale formation Kernel

来源：评论

学校读者我要写书评

暂无评论

Robust Affine iterative closest point algorithm Based on Correntropy for 2D point Set Registration

Robust Affine Iterative Closest Point Algorithm Based on Cor...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wu, Zongze Chen, Hongchen Du, Shaoyi South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (纸本)9781509006199

The traditional affine iterative closest point (ICP) algorithm is fast and accuracy for affine registration of point sets, but it performs worse when the point sets with large outliers. This paper introduces a novel algorithm based on correntropy for affine registration of point sets with outliers. First, a novel objective function is proposed by introducing the maximum correntropy criterion (MCC) because of the outlier-rejection property of correntropy. Then, a new affine ICP algorithm is proposed to solve this energy function. This method uses a simple iterative algorithm and computes the affine transformation quickly at each iterative step. Similar to the ICP algorithm, this new algorithm converges monotonically to a local maximum for any given initial parameters. Experimental results demonstrate that our algorithm has the high speed and accuracy for affine registration with outliers compared with the traditional ICP algorithm and the state-of-the-art algorithms.

关键词： iterative closest point algorithm correntropy outliers affine registration

来源：评论

学校读者我要写书评

暂无评论

3D point cloud matching based on principal component analysis and iterative closest point algorithm

3D point cloud matching based on principal component analysi...

引用

International Conference on Audio, Language and Image Processing, ICALIP

作者： Chi Yuan Xiaoqing Yu Ziyue Luo Institute of Smart City Shanghai University Shanghai China

ISBN: (纸本)9781509006557

point cloud matching is one of the key technologies of optical three-dimensional contour measurement. Most of the point cloud matching without landmark used the iterative closest point algorithm. In order to improve the performance of the iterative closest point algorithm, the two-step iterative closest point algorithm was proposed. The improved algorithm is divided into a rough matching step and accurate matching step. Rough matching used the principal component analysis algorithm, while the fine matching used the improved iterative closest point algorithm. Compared with the classic iterative closest point algorithm, the improved algorithm can match the partial coincident point cloud. At the same time, the experiment can validate the effectiveness of the proposed algorithm.

关键词： Three-dimensional displays iterative closest point algorithm algorithm design and analysis Convergence Quaternions Principal component analysis Transforms

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：