Jove
Visualize
Contact Us

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
Bernoulli's Equation for Flow Along a Streamline01:30

Bernoulli's Equation for Flow Along a Streamline

Bernoulli's equation relates the energy conservation in a fluid moving along a streamline. The equation applies to incompressible and inviscid fluids under steady flow. For such a flow, Newton's second law is applied to a small fluid element, which experiences forces due to pressure differences, gravity, and velocity variations. The force balance leads to the following form of Bernoulli's equation:

You might also read

Related Articles

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

Sort by
Same author

Sequential sensor selection for the localization of acoustic sources by sparse Bayesian learning.

The Journal of the Acoustical Society of America·2022
Same author

Eigenvalues of autocovariance matrix: A practical method to identify the Koopman eigenfrequencies.

Physical review. E·2022
Same author

Stochastic flow approach to model the mean velocity profile of wall-bounded flows.

Physical review. E·2019
Same author

Bayesian estimation of turbulent motion.

IEEE transactions on pattern analysis and machine intelligence·2013
Same author

Stochastic uncertainty models for the luminance consistency assumption.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2011
Same author

A stochastic filtering technique for fluid flow velocity fields tracking.

IEEE transactions on pattern analysis and machine intelligence·2009
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

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

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Updated: May 26, 2026

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

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Bayesian inference of models and hyperparameters for robust optical-flow estimation.

Patrick Héas1, Cédric Herzet, Etienne Mémin

  • 1INRIA Centre de Rennes Bretagne Atlantique, Rennes, France. Patrick.Heas@inria.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 15, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian framework for optimal optical-flow estimation by inferring models and hyperparameters. The method improves accuracy over manual tuning for fluid flows and standard databases.

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

Related Experiment Videos

Last Updated: May 26, 2026

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

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

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

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Accurate optical-flow estimation is vital for analyzing motion in image sequences.
  • Current methods often require manual tuning of parameters, limiting performance.
  • Selecting appropriate models and hyperparameters remains a significant challenge.

Purpose of the Study:

  • To develop a generic Bayesian framework for optimal optical-flow estimation.
  • To automate the selection of models and hyperparameters.
  • To enhance the accuracy and robustness of optical-flow estimation.

Main Methods:

  • A hierarchical Bayesian model is proposed, linking image intensity, velocity field, hyperparameters, and motion models.
  • Inference is performed using maximization of marginalized a posteriori probability distributions across three levels.
  • The framework integrates motion estimation, hyperparameter inference, and model selection.

Main Results:

  • The proposed inference strategy significantly outperforms manual tuning of parameters.
  • Superior results were achieved compared to state-of-the-art methods on fluid flow sequences and the Middlebury database.
  • The method effectively infers regularization coefficients and hyperparameters for robust statistics.

Conclusions:

  • The Bayesian framework offers a principled approach to optical-flow estimation.
  • Automated hyperparameter and model selection lead to improved accuracy.
  • This method provides a robust and adaptable solution for various image analysis tasks.