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

Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

1.6K
There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
TCD is the earliest and most widely used detector that operates by measuring the changes in the thermal conductivity of the carrier gas. When a sample compound enters the detector,...
1.6K
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

2.0K
Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
2.0K
Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

1.2K
In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
1.2K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

584
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
584
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.0K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
2.0K
High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

1.7K
The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Balancing Communication and Acceleration: Exact One-to-One Optimization for Distributed Multiagent Learning Systems.

IEEE transactions on cybernetics·2026
Same author

Clouclip combined with a questionnaire on the influence factors of myopia in children.

Frontiers in pediatrics·2023
Same author

AtMYBS1 negatively regulates heat tolerance by directly repressing the expression of MAX1 required for strigolactone biosynthesis in Arabidopsis.

Plant communications·2023
Same author

The impact of non-adiabatic effects on reaction dynamics: a study based on the adiabatic and non-adiabatic potential energy surfaces of CaH<sub>2</sub><sup></sup>.

Physical chemistry chemical physics : PCCP·2023
Same author

Applications of ion chromatography in urine analysis: A review.

Journal of chromatography. A·2023
Same author

Crystal structure of Tudor domain of TDRD3 in complex with a small molecule antagonist.

Biochimica et biophysica acta. Gene regulatory mechanisms·2023
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 2, 2026

Additive Manufacturing-Enabled Low-Cost Particle Detector
06:05

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

2.4K

A FAST-BRISK Feature Detector with Depth Information.

Yanli Liu1, Heng Zhang2, Hanlei Guo3

  • 1School of Information Engineering, East China Jiaotong University, Nanchang 330013, China. hbliuyanli@126.com.

Sensors (Basel, Switzerland)
|November 16, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces BRISK_D, a new algorithm for RGB-D cameras that uses depth information to improve keypoint detection. BRISK_D enhances robotic vision by refining keypoint scale and space for better performance.

Keywords:
BRISK (Binary Robust Invariant Scalable Keypoints)depth informationrotation invariancescale factorscale invariance

More Related Videos

Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.8K
Handheld Metal Detector Screening for Metallic Foreign Body Ingestion in Children
04:55

Handheld Metal Detector Screening for Metallic Foreign Body Ingestion in Children

Published on: September 11, 2018

11.3K

Related Experiment Videos

Last Updated: Feb 2, 2026

Additive Manufacturing-Enabled Low-Cost Particle Detector
06:05

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

2.4K
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.8K
Handheld Metal Detector Screening for Metallic Foreign Body Ingestion in Children
04:55

Handheld Metal Detector Screening for Metallic Foreign Body Ingestion in Children

Published on: September 11, 2018

11.3K

Area of Science:

  • Computer Vision
  • Robotics
  • Image Processing

Background:

  • RGB-D cameras provide both color and depth data, valuable for robotics and vision.
  • Existing feature detection methods like SURF and BRISK have limitations in scale and depth utilization.

Purpose of the Study:

  • Introduce the novel BRISK_D algorithm for enhanced feature detection using RGB-D data.
  • Evaluate BRISK_D's performance against established algorithms like SURF and BRISK.

Main Methods:

  • Keypoint detection using the Features from Accelerated Segment Test (FAST) algorithm.
  • Refinement of keypoint location in scale and space, incorporating depth information directly.
  • Comparative analysis of SURF, BRISK, and BRISK_D under various image transformations (scaling, rotation, perspective, blur).

Main Results:

  • BRISK_D effectively utilizes depth information for keypoint scale computation.
  • The algorithm demonstrates robust performance across scaling, rotation, perspective, and blur conditions.
  • BRISK_D shows competitive or superior performance compared to SURF and BRISK in experimental evaluations.

Conclusions:

  • BRISK_D offers an efficient and effective method for feature detection in RGB-D imagery.
  • The integration of depth information significantly enhances keypoint accuracy and algorithm robustness.
  • BRISK_D presents a promising advancement for robotic and vision applications requiring precise spatial understanding.