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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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...
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...
Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

You might also read

Related Articles

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

Sort by
Same author

Prevalence of asymptomatic bacteriuria in high-risk hematological patients and its association with bacteremia: A prospective observational study on the need for antibiotic treatment.

European journal of internal medicine·2026
Same author

Systemic anticancer therapy near the end of life: an analysis of factors influencing treatment in advanced cancer patients.

ESMO open·2025
Same author

Positive Neuroblastoma differentiation with AC electrical-stimulation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Hospital discharges (MBDS) from Takotsubo syndrome in Spain. Regional differences (2008-2021).

Revista clinica espanola·2025
Same author

Involving youth with intellectual and/or developmental disabilities as collaborators in a comparative effectiveness trial: A community-engaged research approach.

Contemporary clinical trials communications·2024
Same author

Multimodal assessment improves neuroprognosis performance in clinically unresponsive critical-care patients with brain injury.

Nature medicine·2024

Related Experiment Video

Updated: Jul 7, 2026

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

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Dense estimation and object-based segmentation of the optical flow with robust techniques.

E Mémin1, P Pérez

  • 1Université de Bretagne Sud, Vannes, France. memin@irisa.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
Summary
This summary is machine-generated.

This study presents a novel method for estimating motion and segmenting image sequences. The approach accurately recovers apparent velocity fields and object boundaries using robust optimization and deformable curves.

More Related Videos

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

Related Experiment Videos

Last Updated: Jul 7, 2026

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

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

Area of Science:

  • Computer Vision
  • Image Processing
  • Scientific Computing

Background:

  • Accurate motion estimation is crucial for analyzing image sequences.
  • Existing methods often struggle with discontinuities and require complex optimization.
  • Object segmentation from motion information remains a challenging task.

Purpose of the Study:

  • To develop a robust method for recovering apparent velocity fields in image sequences.
  • To integrate object-based segmentation with motion estimation.
  • To handle discontinuities in flow fields effectively.

Main Methods:

  • Minimizing an objective function with robust optical flow and discontinuity-preserving smoothness constraints.
  • Employing an efficient deterministic multigrid procedure for nonconvex minimization.
  • Extending the model with a flexible object-based segmentation using deformable closed curves.

Main Results:

  • The proposed method achieves fast convergence and high-quality estimates of velocity fields.
  • It successfully reveals large discontinuity structures within flow fields.
  • Experimental results demonstrate effective segmentation of objects in synthetic and natural image sequences.

Conclusions:

  • The developed approach provides a robust and efficient solution for combined motion estimation and object segmentation.
  • The method's ability to handle discontinuities and its flexibility make it suitable for various image sequence analysis tasks.
  • Further analysis of parameter sensitivity is provided for practical application.