Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

587
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
587
Convolution Properties I01:20

Convolution Properties I

602
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
602
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Econometric Views (EViews)01:29

Econometric Views (EViews)

578
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
578
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Cranial Bones: Lateral View01:27

Cranial Bones: Lateral View

4.5K
The lateral view of the cranium is dominated by temporal, sphenoid, and ethmoid bones.
The temporal bone forms the lower lateral side of the skull. The temporal bone is subdivided into several regions. The flattened upper portion is the squamous portion of the temporal bone. Below this area and projecting anteriorly is the zygomatic process of the temporal bone, which forms the posterior portion of the zygomatic arch. Posteriorly is the mastoid portion of the temporal bone. Projecting...
4.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

One-pot formation of chiral polysubstituted 3,4-dihydropyrans via a novel organocatalytic domino sequence involving alkynal self-condensation.

Organic letters·2012
Same author

Non-invasive microelectrode cadmium flux measurements reveal the spatial characteristics and real-time kinetics of cadmium transport in hyperaccumulator and nonhyperaccumulator ecotypes of Sedum alfredii.

Journal of plant physiology·2012
Same author

NO inhibitory guaianolide-derived terpenoids from Artemisia argyi.

Fitoterapia·2012
Same author

Rac1+ cells distributed in accordance with CD 133+ cells in glioblastomas and the elevated invasiveness of CD 133+ glioma cells with higher Rac1 activity.

Chinese medical journal·2012
Same author

Selective adsorption of Hg(II) by γ-radiation synthesized silica-graft-vinyl imidazole adsorbent.

Journal of hazardous materials·2012
Same author

Reconciliation of sequence data and updated annotation of the genome of Agrobacterium tumefaciens C58, and distribution of a linear chromosome in the genus Agrobacterium.

Applied and environmental microbiology·2012
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Feb 3, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

3D Point Cloud Recognition Based on a Multi-View Convolutional Neural Network.

Le Zhang1,2, Jian Sun3,4, Qiang Zheng5,6

  • 1State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China. lezhang0829@stu.xjtu.edu.cn.

Sensors (Basel, Switzerland)
|November 2, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for recognizing 3D objects using multiple 2.5D lidar point clouds. This approach effectively processes lidar data without 3D reconstruction, improving object recognition accuracy.

Keywords:
convolutional neural networkmulti-viewpoint cloudsrecognition

More Related Videos

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
08:28

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays

Published on: April 26, 2018

6.5K
Engineered 3D Silk-collagen-based Model of Polarized Neural Tissue
06:17

Engineered 3D Silk-collagen-based Model of Polarized Neural Tissue

Published on: October 23, 2015

12.9K

Related Experiment Videos

Last Updated: Feb 3, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
08:28

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays

Published on: April 26, 2018

6.5K
Engineered 3D Silk-collagen-based Model of Polarized Neural Tissue
06:17

Engineered 3D Silk-collagen-based Model of Polarized Neural Tissue

Published on: October 23, 2015

12.9K

Area of Science:

  • Computer Vision
  • Robotics
  • Geospatial Technology

Background:

  • Object recognition from 3D lidar point clouds is challenging.
  • Lidar data is often acquired as 2.5D point clouds from single views, not complete 3D objects.

Purpose of the Study:

  • To develop a novel method for 3D object recognition using multi-view 2.5D lidar point clouds.
  • To address the limitations of traditional 3D point cloud recognition methods.

Main Methods:

  • Proposed a new representation for 3D point clouds using multiple 2.5D views.
  • Generated multi-view 2.5D point cloud data using the Point Cloud Library (PCL).
  • Designed a multi-view convolutional neural network for recognition, fusing features from all views.

Main Results:

  • The proposed model achieves excellent recognition performance on lidar point clouds.
  • The method eliminates the need for 3D reconstruction and point cloud preprocessing.
  • Demonstrated effective global feature descriptor learning through view fusion.

Conclusions:

  • The developed approach effectively solves the recognition problem for lidar point clouds.
  • This method offers significant practical value in point cloud processing applications.
  • The multi-view 2.5D approach provides a robust solution for lidar data recognition.