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

Light Acquisition02:16

Light Acquisition

9.3K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
9.3K
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

12.1K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
12.1K
Deconvolution01:20

Deconvolution

534
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
534
Flame Photometry: Lab01:16

Flame Photometry: Lab

829
In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
829
Color Vision01:24

Color Vision

1.3K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.3K

You might also read

Related Articles

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

Sort by
Same author

An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse.

Plants (Basel, Switzerland)·2023
Same author

Roots' Drought Adaptive Traits in Crop Improvement.

Plants (Basel, Switzerland)·2022
Same author

CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements.

Biosensors·2022
Same author

Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping.

The New phytologist·2022

Related Experiment Video

Updated: Jan 11, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

17.1K

LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement.

Pasindu Ranasinghe1, Dibyayan Patra1, Bikram Banerjee2

  • 1School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a new method for creating 3D color point clouds from LiDAR and multiple cameras, even in low light. This hardware-agnostic system enhances spatial understanding by improving detail recovery in challenging conditions.

Keywords:
360° coveragedata fusionlow-light image enhancementmulti-camera integrationobject-free calibrationpoint cloud colourisationsingle-shot calibration

More Related Videos

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

452
Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

11.1K

Related Experiment Videos

Last Updated: Jan 11, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

17.1K
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

452
Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

11.1K

Area of Science:

  • Computer Vision
  • Robotics
  • 3D Sensing

Background:

  • Combining camera and LiDAR data improves spatial understanding.
  • Existing methods struggle with low-light conditions and require specialized calibration.

Purpose of the Study:

  • To develop a hardware-agnostic method for generating colorized point clouds from mechanical LiDAR using multiple cameras.
  • To enhance robustness in low-light conditions through integrated image enhancement.
  • To automate the calibration process, removing the need for specialized targets.

Main Methods:

  • A novel fusion pipeline integrating a low-light image enhancement module.
  • Hardware-agnostic approach for mechanical LiDAR and multiple camera inputs.
  • Automatic computation of extrinsic calibration parameters (geometric transformation).
  • Color correction for uniform camera feeds before fusion.

Main Results:

  • Achieved real-time performance for colorized point cloud generation.
  • Demonstrated robust performance and reliable colorization in very low illumination.
  • Successfully recovered scene details typically undetectable in low light.
  • Validated with a Velodyne Puck Hi-Res LiDAR and a four-camera setup.

Conclusions:

  • The developed methodology offers a robust and efficient solution for 3D spatial understanding.
  • The system significantly enhances detail recovery in low-light environments.
  • The hardware-agnostic and automated calibration features streamline deployment.