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

Data Collection by Observations01:08

Data Collection by Observations

12.5K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
12.5K
Spin–Spin Coupling Constant: Overview01:08

Spin–Spin Coupling Constant: Overview

997
In bromoethane, the three methyl protons are coupled to the two methylene protons that are three bonds away. In accordance with the n+1 rule, the signal from the methyl protons is split into three peaks with 1:2:1 relative intensities. The methylene protons appear as a quartet, with the relative intensities of 1:3:3:1.
Qualitatively, any spin plus-half nucleus polarizes the spins of its electrons to the minus-half state. Consequently, the paired electron in the hydrogen–carbon bond must...
997
Spindle Assembly02:50

Spindle Assembly

3.8K
Spindle assembly occurs through three, often coexisting, pathways – the centrosome-mediated pathway, the chromatin-mediated pathway, and the microtubule-mediated pathway – collectively contributing to form a robust spindle apparatus.
In most cells, centrosomes are the primary microtubule nucleation centers. In the centrosome-mediated pathway, the G2-prophase transition triggers centrosome maturation and increased microtubule nucleation. Progressive nucleation results in a...
3.8K
The Spindle Assembly Checkpoint02:19

The Spindle Assembly Checkpoint

3.2K
The spindle assembly checkpoint is a molecular surveillance mechanism ensuring the fidelity of chromosome segregation during anaphase. The checkpoint monitors the completion of all the prerequisite steps before chromosome segregation to determine whether the segregation process should proceed or be delayed.
Many proteins function together to control the spindle assembly checkpoint. Mutations affecting these proteins may allow cells to proceed into anaphase prematurely, resulting in the...
3.2K
Cluster Sampling Method01:20

Cluster Sampling Method

12.4K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.4K
Data Collection I01:30

Data Collection I

6.5K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
6.5K

You might also read

Related Articles

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

Sort by
Same author

Analysis of Machine Learning-Based Investigation Into Multivariate Factors of Team Performance in Serious Games: Cross-Sectional Retrospective Study.

JMIR serious games·2026
Same author

Wavefront estimation through structured detection in laser scanning microscopy.

Biomedical optics express·2026
Same author

Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.

Journal of chemical information and modeling·2025
Same author

An AI-Driven Telemonitoring System Exploiting Sensorized Glasses and Insoles to Correlate Cognitive Load and Mobility During ADLs.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Corrigendum: How path integration abilities of blind people change in different exploration conditions.

Frontiers in neuroscience·2024
Same author

Unsupervised Active Visual Search With Monte Carlo Planning Under Uncertain Detections.

IEEE transactions on pattern analysis and machine intelligence·2024

Related Experiment Video

Updated: Aug 28, 2025

Synthetic Spider Silk Production on a Laboratory Scale
13:36

Synthetic Spider Silk Production on a Laboratory Scale

Published on: July 18, 2012

26.9K

An online yarn spinning dataset.

Noman Haleem1, Matteo Bustreo1, Alessio Del Bue1

  • 1Pattern Analysis & Computer Vision, Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova 16152, Italy.

Data in Brief
|September 16, 2022
PubMed
Summary

This study introduces a new dataset of yarn spinning videos for evaluating image processing and computer vision models in textile quality testing. The dataset aids in developing automated defect detection systems for improved yarn production.

Keywords:
Image acquisitionOnline quality controlTextile technologyYarn defectsYarn spinning

More Related Videos

Electrospinning Fundamentals: Optimizing Solution and Apparatus Parameters
07:57

Electrospinning Fundamentals: Optimizing Solution and Apparatus Parameters

Published on: January 21, 2011

65.1K
Microfluidic Dry-spinning and Characterization of Regenerated Silk Fibroin Fibers
08:28

Microfluidic Dry-spinning and Characterization of Regenerated Silk Fibroin Fibers

Published on: September 4, 2017

10.0K

Related Experiment Videos

Last Updated: Aug 28, 2025

Synthetic Spider Silk Production on a Laboratory Scale
13:36

Synthetic Spider Silk Production on a Laboratory Scale

Published on: July 18, 2012

26.9K
Electrospinning Fundamentals: Optimizing Solution and Apparatus Parameters
07:57

Electrospinning Fundamentals: Optimizing Solution and Apparatus Parameters

Published on: January 21, 2011

65.1K
Microfluidic Dry-spinning and Characterization of Regenerated Silk Fibroin Fibers
08:28

Microfluidic Dry-spinning and Characterization of Regenerated Silk Fibroin Fibers

Published on: September 4, 2017

10.0K

Area of Science:

  • Textile Engineering
  • Computer Vision
  • Image Processing

Background:

  • Accurate textile yarn quality assessment is crucial for manufacturing.
  • Existing methods for yarn defect detection can be labor-intensive and subjective.
  • Developing automated, image-based systems offers potential for objective and efficient quality control.

Purpose of the Study:

  • To present a novel, comprehensive dataset of online yarn spinning videos.
  • To facilitate the evaluation and benchmarking of image processing and computer vision algorithms for textile yarn quality analysis.
  • To support the development of automated systems for online defect detection in yarn production.

Main Methods:

  • Acquisition of continuous yarn spinning videos (59.05, 29.5, 14.76 tex cotton yarns) using a customized imaging system on a ring spinning frame.
  • Recording three 250-meter videos per yarn variety, each containing 20,200 image frames and sized at 29.26 GB.
  • Generation of ground truth labels for yarn quality parameters through physical testing on an industrial yarn quality tester.

Main Results:

  • The dataset comprises high-resolution video data of cotton yarn spinning.
  • Ground truth labels were generated for various yarn quality parameters.
  • The dataset has been preliminarily used to validate computer vision models for online nep defect detection.

Conclusions:

  • The presented online yarn spinning dataset is a valuable resource for research and development in textile quality control.
  • It enables the evaluation of diverse imaging-based algorithms for both online and offline yarn quality testing.
  • Future applications include performance assessment of various defect detection systems and quality assurance technologies.