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

Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

1.0K
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
1.0K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

410
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
410
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

504
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...
504
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

1.0K
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
1.0K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

325
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
325
Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

1.9K
Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Pseudo-Rendering for Resolution and Topology-Invariant Cortical Parcellation.

Machine learning in medical imaging. MLMI (Workshop)·2025
Same author

QUANTIFYING WHITE MATTER HYPERINTENSITY AND BRAIN VOLUMES IN HETEROGENEOUS CLINICAL AND LOW-FIELD PORTABLE MRI.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

Multi-View Disparity Estimation Using the Gradient Consistency Model.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

A new compression strategy to reduce the size of nanopore sequencing data.

Genome research·2025
Same author

Synthetic data in generalizable, learning-based neuroimaging.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series.

The journal of machine learning for biomedical imaging·2024
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jan 5, 2026

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.3K

Graph Laplacian Regularization for Robust Optical Flow Estimation.

Sean I Young, Aous T Naman, David Taubman

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 16, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces graph Laplacian regularization for accurate optical flow estimation. Dense graphs improve noise filtering while preserving flow discontinuities, leading to robust and efficient results.

    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

    17.1K
    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: Jan 5, 2026

    Profiling Maternal Behavior Responses During Whole-Brain Imaging
    07:12

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    1.3K
    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

    17.1K
    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:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Optical flow estimation is crucial for understanding motion in image sequences.
    • Traditional methods often struggle with noise and discontinuities, impacting accuracy.
    • Graph Laplacian regularization offers a promising approach for enhancing optical flow robustness.

    Purpose of the Study:

    • To propose a novel graph Laplacian regularization method for robust optical flow estimation.
    • To analyze the spectral properties of dense graph Laplacians for improved performance.
    • To enhance the handling of flow discontinuities and occlusions in optical flow computation.

    Main Methods:

    • Analysis of spectral properties of dense graph Laplacians.
    • Development of a robust optical flow estimation method using Gaussian graph Laplacians.
    • Application of iteratively reweighted least-squares with graph edge reweighting.
    • Utilization of the Welsch loss function for discontinuity preservation and occlusion handling.

    Main Results:

    • Dense graphs demonstrate a superior trade-off between noise filtering and discontinuity preservation compared to standard Laplacians.
    • The proposed Gaussian graph Laplacian method achieves robust and efficient optical flow estimation.
    • Experimental validation on Middlebury and MPI-Sintel datasets confirms the approach's effectiveness.

    Conclusions:

    • Graph Laplacian regularization, particularly with dense graphs, significantly enhances optical flow estimation robustness.
    • The proposed method effectively addresses challenges like noise and flow discontinuities.
    • This work provides a valuable contribution to the field of computer vision and motion analysis.