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

Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

606
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...
606
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.0K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
2.0K
Intracellular Signaling Affects Focal Adhesions01:17

Intracellular Signaling Affects Focal Adhesions

3.6K
Integrins act both as extracellular input receivers and as intracellular processing activators. As their name suggests, integrins are entirely integrated into the membrane structure. Their hydrophobic membrane-spanning regions interact with the phospholipid bilayer's hydrophobic region. These membrane receptors provide extracellular attachment sites for effectors like hormones and growth factors. They activate intracellular response cascades when their effectors are bound and active.
Some...
3.6K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

515
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...
515

You might also read

Related Articles

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

Sort by
Same author

Development and validation of survival nomograms in elder triple-negative invasive ductal breast carcinoma patients.

Expert review of anticancer therapy·2024
Same author

Macrophage-Related Gene Signatures for Predicting Prognosis and Immunotherapy of Lung Adenocarcinoma by Machine Learning and Bioinformatics.

Journal of inflammation research·2024
Same author

[The correlation between preoperative neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio and postoperative recurrence of chronic rhinosinusitis].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2024
Same author

Levitated 2D manipulation on dielectric metasurface by the tuning of polarization states.

Optics letters·2024
Same author

Clinical considerations of CDK4/6 inhibitors in HER2 positive breast cancer.

Frontiers in oncology·2024
Same author

Oxidation of p-toluenesulfonic acid fractionated hybrid Pennisetum by different methods for carboxylated nanocellulose preparation: The evaluation of efficiency and sustainability.

Bioresource technology·2024

Related Experiment Video

Updated: Feb 8, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

Learning Depth from Single Images with Deep Neural Network Embedding Focal Length.

Lei He, Guanghui Wang, Zhanyi Hu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 12, 2018
    PubMed
    Summary

    This study reveals focal length significantly impacts single-image depth estimation. Embedding focal length information into deep learning models improves monocular depth accuracy.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

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

    Published on: December 15, 2023

    1.1K
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

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

    Published on: February 6, 2020

    9.9K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.0K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

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

    Published on: December 15, 2023

    1.1K
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

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

    Published on: February 6, 2020

    9.9K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Single-image depth estimation is crucial for scene understanding.
    • Deep convolutional neural networks have advanced depth estimation accuracy.
    • Inherent ambiguities exist in recovering 3D from 2D images.

    Purpose of the Study:

    • To address the ambiguity between focal length and monocular depth learning.
    • To improve the accuracy of single-image depth estimation by incorporating focal length.
    • To propose a novel deep neural network for accurate depth recovery.

    Main Methods:

    • Proved the ambiguity between focal length and monocular depth learning.
    • Generated a synthetic dataset with varying focal lengths from fixed-focal-length datasets.
    • Developed a novel deep neural network fusing middle-level information and incorporating focal length.
    • Used the generated varying-focal-length dataset for training and inference.

    Main Results:

    • Focal length has a significant influence on accurate depth recovery.
    • The proposed method outperforms state-of-the-art methods on fixed-focal-length datasets.
    • Learned monocular depth significantly improved when focal length was embedded.

    Conclusions:

    • Embedding focal length information is critical for accurate monocular depth estimation.
    • The proposed deep neural network effectively fuses information for improved depth recovery.
    • The approach offers a significant advancement in single-image depth estimation techniques.