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

Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

456
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
456
Areas Within Irregular Boundaries01:26

Areas Within Irregular Boundaries

466
Calculating areas within irregular boundaries, such as along rivers or curved roads, is crucial in various fields, including surveying, engineering, and environmental management. Surveyors often begin by creating a traverse, a connected series of straight lines approximating the area's boundary. The coordinates of each traverse point are essential for calculating the enclosed area. The double meridian distance formula is a widely used technique for this purpose. This method utilizes the...
466

You might also read

Related Articles

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

Sort by
Same author

Reconstructing Three-Dimensional Models of Interacting Humans.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms.

Computers in biology and medicine·2024
Same author

A regime switch analysis on Covid-19 in Romania.

Scientific reports·2022
Same author

Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Driven by Vision: Learning Navigation by Visual Localization and Trajectory Prediction.

Sensors (Basel, Switzerland)·2021
Same author

Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection.

Sensors (Basel, Switzerland)·2020
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Apr 4, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.8K

Generalized Boundaries from Multiple Image Interpretations.

Marius Leordeanu, Rahul Sukthankar, Cristian Sminchisescu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a generalized boundary detection method (Gb) that accurately identifies object edges and occlusion boundaries. It achieves state-of-the-art results with lower computational cost, enhancing computer vision tasks.

    More Related Videos

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.9K
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.8K

    Related Experiment Videos

    Last Updated: Apr 4, 2026

    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    6.8K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.9K
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.8K

    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Boundary detection is crucial for tasks like segmentation, object recognition, and symmetry detection.
    • Existing methods often face limitations in accuracy or computational efficiency.

    Purpose of the Study:

    • To propose a generalized boundary detection method with a closed-form solution.
    • To improve accuracy and reduce computational cost in boundary detection algorithms.

    Main Methods:

    • Developed a generalized boundary detection (Gb) method combining low-level and mid-level image representations.
    • Solved boundary detection using a single eigenvalue problem for optimal continuous boundary orientation and strength.
    • Introduced soft-segmentation and contour grouping for enhanced performance.

    Main Results:

    • Achieved state-of-the-art results in boundary detection.
    • Significantly reduced computational cost compared to existing methods.
    • Demonstrated improved accuracy with complementary soft-segmentation and contour grouping components.

    Conclusions:

    • The generalized boundary detection method offers an efficient and accurate solution for various computer vision applications.
    • The proposed complementary components further enhance boundary detection performance.
    • Gb provides a robust framework for edge and occlusion boundary localization.