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

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.0K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.0K
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.8K
Random Error01:04

Random Error

9.8K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.8K
Margin of Error01:27

Margin of Error

7.6K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.6K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.5K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.5K
Standard Error of the Mean01:13

Standard Error of the Mean

12.4K
The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
12.4K

You might also read

Related Articles

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

Sort by
Same author

Full-length transcriptomic profiling reveals age-associated isoform remodeling and altered coding potential in the mouse ovary.

Journal of ovarian research·2026
Same author

Integrated Multiomics Enabled by Sequential Extraction for Comprehensive Molecular Profiling of Small Extracellular Vesicles.

Analytical chemistry·2026
Same author

Transcriptome-Metabolome Integration Reveals Key Regulators of Isoflavone Biosynthesis in Soybean for Enhanced Nutritional Quality.

Journal of agricultural and food chemistry·2026
Same author

<i>N</i>-Sulfinyl Phthalimides as Modular Sulfinyl Radical Precursors: PRE-Guided Chemoselective Alkene Difunctionalization and Radical Cross-Coupling.

Journal of the American Chemical Society·2026
Same author

Identification of Genes Associated with Seed Weight and Development of Functional Markers for <i>GmUBP15</i> in <i>Glycine max</i>.

Biology·2026
Same author

Maternal LDHB Safeguards Redox Balance and Developmental Competence During Preimplantation Embryo Cleavage.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026

Related Experiment Video

Updated: Feb 8, 2026

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors
08:32

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors

Published on: January 3, 2017

23.2K

Rectification of Images Distorted by Microlens Array Errors in Plenoptic Cameras.

Suning Li1, Yanlong Zhu2, Chuanxin Zhang3

  • 1School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China. 16b902036@stu.hit.edu.cn.

Sensors (Basel, Switzerland)
|June 26, 2018
PubMed
Summary
This summary is machine-generated.

Plenoptic cameras capture 4D light fields but suffer from microlens array errors. This study introduces a novel rectification method using white light-field images to correct distortions and improve image quality for accurate 3D reconstruction.

Keywords:
calibrationdistortion rectificationlight-field image processingmicrolens arrayplenoptic camera

More Related Videos

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

17.2K
Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

8.0K

Related Experiment Videos

Last Updated: Feb 8, 2026

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors
08:32

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors

Published on: January 3, 2017

23.2K
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

17.2K
Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

8.0K

Area of Science:

  • Optics and Photonics
  • Computational Imaging
  • Computer Vision

Background:

  • Plenoptic cameras capture 4D light-field data, crucial for 3D reconstruction.
  • Surface errors in microlens arrays (MLAs) degrade image quality and distort light-field information.

Purpose of the Study:

  • To develop and evaluate a method for local rectification of distorted images from plenoptic cameras caused by MLA surface errors.
  • To improve the precision of light-field data for accurate object reconstruction.

Main Methods:

  • A novel method involving microlens center calibration, geometric rectification, and grayscale rectification using white light-field images.
  • Simulation of imaging experiments to analyze rectification accuracy for various error types and sizes.

Main Results:

  • The proposed method significantly improves the quality of rectified images.
  • Accurate correction of overall and local surface errors, including geometric and grayscale distortions.
  • Demonstrated effectiveness in providing precise light-field data.

Conclusions:

  • The developed local rectification method effectively addresses MLA surface errors in plenoptic cameras.
  • The technique enhances the reliability of light-field data for high-fidelity 3D reconstruction.
  • This approach offers a practical solution for improving plenoptic imaging systems.