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

Integrals of Vector Functions01:23

Integrals of Vector Functions

Vector-valued functions provide a convenient framework for describing motion in space when both magnitude and direction are important. A drone’s velocity at any instant has a direction and a speed, and as the drone moves, both can change. A vector-valued function captures this behavior by assigning to each time a vector whose components are real-valued functions. Each component represents motion along a particular axis in space. Such functions can describe motion in either two-dimensional or...
Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
Consider an example to calculate the velocity and position from the acceleration function. A motorboat is traveling at a constant velocity of 5.0 m/s when it starts to decelerate to arrive at the dock. Its acceleration is...
Triple Integrals over General Regions01:28

Triple Integrals over General Regions

Triple integrals over general bounded regions extend the concept of double integrals from planar domains to three-dimensional solids. A solid region E in space is commonly enclosed within a rectangular box B, and a continuous function f(x, y, z) is integrated over the region by defining F such that it coincides with f on E and is zero outside the solid. The triple integral is therefore expressed as\begin{equation*}\iiint_E f(x,y,z) dV \end{equation*}The existence of the integral requires that f...
Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...
Triple Integrals in Rectangular Coordinates01:23

Triple Integrals in Rectangular Coordinates

Triple integrals provide a method for calculating the accumulated value of a function over a three-dimensional region. Common applications include computing volume, mass, and other physical quantities that vary with position. The fundamental idea is to partition a solid region into small rectangular boxes, evaluate the function at sample points within each box, and sum the contributions. As the partitions become finer, this triple Riemann sum approaches the exact value of the triple integral.In...
Triple Integrals in Cylindrical Coordinates01:28

Triple Integrals in Cylindrical Coordinates

Cylindrical coordinates describe a point in three-dimensional space using three values: radial distance, angle, and height. The height gives the position above the xy-plane, the radial distance measures how far the point is from the z-axis, and the angle describes the point’s direction from the positive x-axis in the xy-plane. This system is especially useful for regions with circular symmetry because it matches the natural geometry of cylinders, disks, and circular tanks.To calculate volume,...

You might also read

Related Articles

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

Sort by
Same author

AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms.

Biomedical optics express·2026
Same author

Morphological investigation of astrocyte brain cells using quantitative phase imaging.

Biomedical optics express·2026
Same author

Security authentication and tracking of unmanned moving vehicles with optical ID tags.

Optics express·2026
Same author

3D profilometric object detection in turbid water using integral imaging and deep neural networks.

Optics express·2026
Same author

Underwater multidimensional metrology in degraded environments with augmented reality devices.

Optics express·2026
Same author

Dynamic vision-based underwater optical signal detection system in a degraded environment using multi-dimensional integral imaging and deep learning.

Optics express·2026

Related Experiment Video

Updated: Jun 22, 2026

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

Performance of 3D integral imaging with position uncertainty.

Behnoosh Tavakoli, Mehdi Daneshpanah, Bahram Javidi

    Optics Express
    |June 24, 2009
    PubMed
    Summary

    Sensor position uncertainty degrades high-frequency details in Synthetic Aperture Integral Imaging (SAII). Reconstruction distance inversely impacts this degradation, offering insights for improved 3D imaging systems.

    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • 3D Reconstruction

    Background:

    • Synthetic Aperture Integral Imaging (SAII) is a passive 3D imaging technique using multiple perspectives under natural illumination.
    • Practical SAII applications face challenges due to uncertainties in sensor pickup positions for elemental images.
    • Quantifying the impact of these uncertainties is crucial for robust 3D reconstruction.

    Purpose of the Study:

    • To theoretically analyze and simulate the sensitivity of SAII to pickup position uncertainty.
    • To quantify image degradation using the Mean Square Error (MSE) metric.
    • To identify key parameters influencing reconstruction fidelity.

    Main Methods:

    • Theoretical analysis to derive a quantitative measure of image degradation (MSE).

    More Related Videos

    Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
    06:09

    Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

    Published on: March 12, 2021

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
    05:05

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

    Related Experiment Videos

    Last Updated: Jun 22, 2026

    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

    Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
    06:09

    Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

    Published on: March 12, 2021

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
    05:05

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

  • Computer simulations to validate theoretical findings and explore parameter effects.
  • Investigation of the relationship between sensor position uncertainty and 3D reconstruction quality.

    • Main Results:

    • Position uncertainty in SAII significantly degrades the high spatial frequency content of 3D reconstructed images.
    • A clear inverse relationship exists between the reconstruction distance and the calculated degradation metric.
    • Simulation results confirm the theoretical analysis regarding image quality reduction.

    Conclusions:

    • Sensor position uncertainty is a critical factor affecting 3D image quality in SAII systems.
    • Understanding this degradation allows for the development of more resilient SAII algorithms.
    • This study provides the first quantitative analysis of sensor position uncertainty's effects on 3D computational reconstruction in SAII.