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

X-ray Crystallography02:18

X-ray Crystallography

26.2K
The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
26.2K
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
Confidence Intervals01:21

Confidence Intervals

10.8K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.8K
Improper Integrals: Infinite Intervals01:29

Improper Integrals: Infinite Intervals

110
An integral is classified as improper due to an infinite interval when at least one of its limits of integration extends to positive or negative infinity. In such cases, the region under the curve is unbounded, and standard techniques for evaluating definite integrals are not directly applicable. Instead, the improper integral is defined through a limiting process that allows one to determine whether the accumulated area remains finite despite the infinite domain.Application to Exponential...
110
Interval Level of Measurement00:55

Interval Level of Measurement

19.2K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
19.2K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.1K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
10.1K

You might also read

Related Articles

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

Sort by
Same author

Particle Merging-and-Splitting.

IEEE transactions on visualization and computer graphics·2021
Same author

Young Man with Cardiac Arrest Secondary to Undiagnosed Mediastinal Mass: A Case Report.

Clinical practice and cases in emergency medicine·2021
Same author

Wearable 3D Machine Knitting: Automatic Generation of Shaped Knit Sheets to Cover Real-World Objects.

IEEE transactions on visualization and computer graphics·2021
Same author

Patch Textures: Hardware Support for Mesh Colors.

IEEE transactions on visualization and computer graphics·2020
Same author

Mach-RT: A Many Chip Architecture for High Performance Ray Tracing.

IEEE transactions on visualization and computer graphics·2020
Same author

Stochastic Lightcuts for Sampling Many Lights.

IEEE transactions on visualization and computer graphics·2020
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Trace Fear Conditioning in Mice
07:02

Trace Fear Conditioning in Mice

Published on: March 20, 2014

34.0K

Time Interval Ray Tracing for Motion Blur.

Konstantin Shkurko, Cem Yuksel, Daniel Kopta

    IEEE Transactions on Visualization and Computer Graphics
    |July 11, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a novel ray tracing method for realistic motion blur. It analytically approximates blur per ray, avoiding sampling for efficient, noise-free results in computer graphics.

    More Related Videos

    Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung
    11:26

    Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung

    Published on: July 18, 2016

    9.2K
    Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating
    10:39

    Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

    Published on: October 11, 2016

    10.1K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Trace Fear Conditioning in Mice
    07:02

    Trace Fear Conditioning in Mice

    Published on: March 20, 2014

    34.0K
    Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung
    11:26

    Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung

    Published on: July 18, 2016

    9.2K
    Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating
    10:39

    Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

    Published on: October 11, 2016

    10.1K

    Area of Science:

    • Computer Graphics
    • Computational Geometry
    • Image Processing

    Background:

    • Realistic motion blur is crucial for visual fidelity in computer graphics.
    • Existing methods often rely on computationally expensive sampling techniques.

    Purpose of the Study:

    • To develop an efficient and accurate method for computing motion blur in ray tracing.
    • To eliminate noise associated with sampling-based motion blur approximations.

    Main Methods:

    • Associating time intervals with rays for direct intersection evaluation with animated object faces.
    • Representing swept volumes using triangulations for intersection with stationary triangles.
    • Utilizing standard ray tracing acceleration structures without time-dimension modifications.

    Main Results:

    • Analytical approximation of motion blurred visibility per ray.
    • Noise-free motion blur for primary and secondary rays.
    • Framework for emulating camera shutter mechanisms and motion amplification.

    Conclusions:

    • The proposed method offers an efficient and high-quality solution for motion blur in ray tracing.
    • Enables improved visual realism and artistic control in rendering animated scenes.