Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving

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 Flow

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...

You might also read

Related Articles

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

Sort by

Same author

Knowledge Distillation in Object Detection: A Survey from CNN to Transformer.

Sensors (Basel, Switzerland)·2026

Same author

Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer.

Sensors (Basel, Switzerland)·2026

Same author

Object Detection with Transformers: A Review.

Sensors (Basel, Switzerland)·2025

Same author

Optimizing imbalanced learning with genetic algorithm.

Scientific reports·2025

Same author

EventEgo3D++: 3D Human Motion Capture from A Head-Mounted Event Camera.

International journal of computer vision·2025

Same author

Enhancing robustness and generalization in microbiological few-shot detection through synthetic data generation and contrastive learning.

Computers in biology and medicine·2025

Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026

Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026

Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026

Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026

Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026

Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Scene flow based deep network for hand reconstruction using depth images.

Adnan Anwer¹, Jameel Malik¹, Khawar Khurshid²

¹National University of Sciences and Technology (NUST), Islamabad, Pakistan.

Scientific Reports

|September 24, 2025

Summary

This summary is machine-generated.

This study introduces HandFlowNet, a novel pipeline for 3D hand reconstruction using multi-view depth images. It leverages temporal information and scene flow for more stable and accurate hand tracking, achieving state-of-the-art results.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jan 17, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
3D Reconstruction
Machine Learning

Background:

Accurate 3D hand reconstruction is a challenging computer vision problem.
Existing methods often neglect temporal information, limiting stable hand tracking.
Multi-view depth imaging offers rich data for hand pose estimation.

Purpose of the Study:

To develop a novel pipeline, HandFlowNet, for accurate 3D hand reconstruction from consecutive multi-view depth images.
To incorporate temporal information for improved stability in hand tracking.
To achieve state-of-the-art performance on benchmark datasets.

Main Methods:

Converting multi-view depth images into a single point cloud.
Estimating scene flow of hand mesh vertices to deduce temporal information between frames.
Utilizing a graph convolutional network for refining hand mesh vertices with local and global features.

Main Results:

HandFlowNet successfully deduces temporal information from sequential depth frames.
The scene flow is applied as an offset for accurate vertex estimation.
The graph convolutional network refines mesh vertices for enhanced accuracy.
State-of-the-art performance achieved on DexYCB and HO3D benchmarks.

Conclusions:

HandFlowNet provides a robust pipeline for 3D hand reconstruction.
The integration of temporal information significantly enhances hand tracking stability.
The proposed method sets a new benchmark for accuracy in real-world hand pose estimation.