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

Building Three-Dimensional Neuronal Networks Coupled to Micro-Electrode Arrays02:39

Building Three-Dimensional Neuronal Networks Coupled to Micro-Electrode Arrays

475
This video demonstrates the development of a 3D neuronal culture coupled to a planar microelectrode array with hippocampal neurons. The isolated neurons form a 2D network on the microelectrode array's active area and adhere to glass microbeads in a multi-well plate with a membrane insert. Upon transferring these neuron-coated microbeads to the electrode, they organize into an interconnected 3D neural...
475
Visualization of Neural and Vascular Networks in a Chicken Embryo03:33

Visualization of Neural and Vascular Networks in a Chicken Embryo

471
Source: Delalande, J., et.al. Dual Labeling of Neural Crest Cells and Blood Vessels Within Chicken Embryos Using ChickGFP Neural Tube Grafting and Carbocyanine Dye DiI Injection. J. Vis. Exp. (2015)This video demonstrates the transplantation of a GFP-labeled donor neural tube from a stage-matched transgenic chicken embryo into a recipient embryo at the level of somites one to seven, followed by vascular labeling using a lipophilic fluorescent dye. The combined approach allows for direct...
471
Developing a Micro-Tissue-Engineered Neural Network Using a Hydrogel-Based Micro-column02:53

Developing a Micro-Tissue-Engineered Neural Network Using a Hydrogel-Based Micro-column

341
Source: Struzyna, L. A. et. al., Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling. J. Vis. Exp. (2017)This video demonstrates the development of micro-tissue-engineered neural networks using a hydrogel micro-column with an extracellular matrix core. Seeded neuronal aggregates adhere, extend projections, and form structured neural networks, contributing to advancements in neurodevelopment and...
341
End-To-End Deep Neural Network for Salient Object Detection in Complex Environments03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

1.0K
The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental...
1.0K
Deep Neural Networks for Image-Based Dietary Assessment13:19

Deep Neural Networks for Image-Based Dietary Assessment

9.9K
The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image...
9.9K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

220
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
220

You might also read

Related Articles

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

Sort by
Same author

Endogenous near-infrared chemiluminescent manganese ion-driven porphyrin supramolecular platform for the theranostics of thrombosis and ischemic stroke.

RSC advances·2026
Same author

Fluidic Lipid-Bilayer-Enhanced Iontronic Nanopore: Machine-Learning-Driven Ultrasensitive MicroRNA Detection in Cancer Diagnostics.

ACS sensors·2026
Same author

Long-term smoking and early-onset diabetic retinopathy in young-onset type 2 diabetes.

European journal of ophthalmology·2026
Same author

Millimeter-Wave MIMO radar for contactless neonatal heart-rate assessment: performance under common resuscitation-related maneuvers and feasibility in clinical workflow.

Frontiers in pediatrics·2026
Same author

Epidemiological trends and geographic disparities in low back pain burden based on the 2021 GBD study: A cross-sectional analysis.

Medicine·2026
Same author

Inhibition of the <i>in vitro</i> colonic fermentation of cooked gluten by dietary fibers with individual fermentability.

Food & function·2026

Related Experiment Video

Updated: Jan 20, 2026

Building Three-Dimensional Neuronal Networks Coupled to Micro-Electrode Arrays
02:39

Building Three-Dimensional Neuronal Networks Coupled to Micro-Electrode Arrays

475

Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks.

Siyan Zhou1,2, Yanlei Li1,2, Fubo Zhang3

  • 1School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.

Sensors (Basel, Switzerland)
|September 5, 2019
PubMed
Summary

This study introduces an automated neural network method to denoise and regularize 3D building structures from Tomographic SAR (TomoSAR) point clouds. The approach refines surface points for smoother buildings while preserving structural integrity, improving 3D urban imaging quality.

Keywords:
3-D point cloudsdenoisingneural networksregularizationtomographic SAR

More Related Videos

Visualization of Neural and Vascular Networks in a Chicken Embryo
03:33

Visualization of Neural and Vascular Networks in a Chicken Embryo

Published on: June 17, 2025

471
Developing a Micro-Tissue-Engineered Neural Network Using a Hydrogel-Based Micro-column
02:53

Developing a Micro-Tissue-Engineered Neural Network Using a Hydrogel-Based Micro-column

Published on: August 7, 2025

341

Related Experiment Videos

Last Updated: Jan 20, 2026

Building Three-Dimensional Neuronal Networks Coupled to Micro-Electrode Arrays
02:39

Building Three-Dimensional Neuronal Networks Coupled to Micro-Electrode Arrays

475
Visualization of Neural and Vascular Networks in a Chicken Embryo
03:33

Visualization of Neural and Vascular Networks in a Chicken Embryo

Published on: June 17, 2025

471
Developing a Micro-Tissue-Engineered Neural Network Using a Hydrogel-Based Micro-column
02:53

Developing a Micro-Tissue-Engineered Neural Network Using a Hydrogel-Based Micro-column

Published on: August 7, 2025

341

Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Computer Vision

Background:

  • Tomographic SAR (TomoSAR) generates 3D point clouds for urban mapping.
  • TomoSAR data often contains noise, degrading 3D imaging and building reconstruction quality.
  • Current processing methods rely on data segmentation, limiting efficiency and denoising effectiveness.

Purpose of the Study:

  • To develop an automated method for denoising and regularizing 3D building structures from TomoSAR point clouds.
  • To improve the quality of 3D imaging and building reconstruction in urban areas using TomoSAR data.
  • To overcome limitations of existing segmentation-dependent processing techniques.

Main Methods:

  • An automated method using neural networks inspired by regression analysis is proposed.
  • The method refines building surface points by adjusting point heights.
  • It avoids data segmentation and complex parameter adjustments, enhancing automation.

Main Results:

  • The method effectively smooths building surfaces while precisely preserving structural details.
  • It demonstrates commendable performance in denoising TomoSAR point clouds.
  • High automation levels are achieved, simplifying the processing workflow.

Conclusions:

  • The proposed neural network-based regression method significantly enhances the quality of 3D urban building reconstruction from TomoSAR data.
  • This automated approach offers an efficient and effective alternative to segmentation-based methods.
  • The technique shows strong potential for improving remote sensing applications in urban environments.