Search

  • Select Article Type
  • Abstract Supplements
  • Blood Group Review
  • Call to Arms
  • Hypothesis
  • In Memoriam
  • Interview
  • Introduction
  • Letter to the Editor
  • Short Report
  • abstract
  • Abstracts
  • Article
  • book-review
  • case-report
  • case-study
  • Clinical Practice
  • Commentary
  • Conference Presentation
  • conference-report
  • congress-report
  • Correction
  • critical-appraisal
  • Editorial
  • Editorial Comment
  • Erratum
  • Events
  • Letter
  • Letter to Editor
  • mini-review
  • minireview
  • News
  • non-scientific
  • Obituary
  • original-paper
  • original-report
  • Original Research
  • Pictorial Review
  • Position Paper
  • Practice Report
  • Preface
  • Preliminary report
  • Product Review
  • rapid-communication
  • Report
  • research-article
  • Research Communicate
  • research-paper
  • Research Report
  • Review
  • review -article
  • review-article
  • review-paper
  • Review Paper
  • Sampling Methods
  • Scientific Commentary
  • short-communication
  • short-report
  • Student Essay
  • Varia
  • Welome
  • Select Journal
  • In Jour Smart Sensing And Intelligent Systems
  • International Journal Advanced Network Monitoring Controls

 

Article | 02-April-2020

Review of 3D Point Cloud Data Segmentation Methods

I. INTRODUCTION Image segmentation is one of the basic research directions of computer vision, and its purpose is to subdivide a digital image into multiple regions with similar properties[1]. Segmentation of 2D images has more than 50 years of research history, but 3D point cloud data is a highly redundant and irregularly ordered structure, point cloud segmentation also faces many challenges. The segmentation of point clouds into foreground and background is a fundamental step in processing 3D

Xiaoyi Ruan, Baolong Liu

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 66–71

Article | 19-April-2021

A Review of Segmentation Technology Based on 3D Point Cloud

I. INTRODUCTION Point cloud is in the same space reference frame to express target spatial distribution and the characteristics of the target surface mass point set, compared with the two-dimensional images, point cloud has its irreplaceable advantages in - depth information, not only avoid the point cloud data encountered in the process of image acquisition of pose, illumination, and itself has abundant spatial information, can effectively express the space objects in size, shape, location and

Wang Xu, Liu Baolong

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 1, 35–40

Article | 30-November-2020

Review of Bounding Box Algorithm Based on 3D Point Cloud

high, so the idea of linear programming is adopted for optimization. For soft body, k-DOP needs to update the deformed leaf node first, and then update the parent node according to the bottom-up method, so k-DOP is suitable for both rigid body and soft body environment. III. SIMULATION AND ANALYSIS This paper simulates the above three bounding box algorithms. The experiment uses VS2107 compilation environment and VTK visualization library. The 3D point cloud data is the rabbit point cloud data

He Siwei, Liu Baolong

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 1, 18–23

Article | 09-April-2018

Lidar Image Classification based on Convolutional Neural Networks

This paper presents a new method of recognition of lidar cloud point images based on convolutional neural network. This experiment uses 3D CAD ModelNet, and generates 3D point cloud data by simulating the scanning process of lidar. The data is divided into cells, and the distance is represented by gray values. Finally, the data is stored as grayscale images. Changing the number of cells dividing point cloud results in different experimental results. Experiments show that the proposed method has

Yang Wenhui, Yu Fan

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 158–162

Article | 02-April-2020

Comparison of Several Different Registration Algorithms

environment and so on. Usually, the point cloud data of three-dimensional objects are acquired from different angles by data acquisition equipment for many times, and the point cloud registration algorithm is used to splice the point clouds of various perspectives into the complete point cloud data. Point cloud registration is an important and difficult part of reverse engineering. The registration degree between point clouds will directly affect the accuracy of the whole 3D model, so point cloud

Lulu Liu, Baolong Liu

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 22–27

research-article | 30-November-2020

Calibration of Structured Light Scanning System

I. INTRODUCTION Stomatology is one of the first disciplines to introduce digital technology, and it is the dominant discipline in digitalization. In 1987, CAD/CAM technology has been applied in clinical denture restoration. The three-dimensional measurement of the dental jaw model is the basis of the oral CAM/CAD system. Only on the basis of obtaining the digital model of the dental jaw by measuring the three-dimensional point cloud, the doctor can use computer-aided technology to design and

Yi Guo, Xiaoyi Ruan

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 2, 56–64

Article | 01-September-2016

RECOGNITION OF THE STACKED OBJECTS FOR BIN PICKING

In this paper, we propose recognition method of the stacked objects for pick-and-place motion. The situation that the objects are stacked miscellaneously in the home is assumed. In the home, the equipment to arrange the objects doesn't exist. Therefore it's necessary to recognize the stacked objects respectively. In this paper, Information on the objects are measured by a laser range finder (LRF). Those information is used as 3-D point cloud, and the objects are recognized by model-base. A

M. Hikizu, S. Mikami, H. Seki

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1177–1188

Article | 07-May-2018

3D Target Recognition Based on Decision Layer Fusion

Target recognition has always been a hot research topic in computer image and pattern recognition. This paper proposes a target recognition method based on decision layer fusion. ModelNet[1]—The 3D CAD model library, which is used to be identified. Features are extracted from the model’s point cloud data and multi-view images. The image is identified using the AlexNet[2] network, The point cloud is identified by the VoxNet[3] network. The fusion algorithm is used in the decision layer to

Ma Xing, Yu Fan, Yu Haige, Wei Yanxi, Yang Wenhui

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 1, 19–22

No Record Found..
Page Actions