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

Labeling DNA Probes03:31

Labeling DNA Probes

7.7K
DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
7.7K
Labeling Emotion01:20

Labeling Emotion

994
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
994
Aggregates Classification01:29

Aggregates Classification

978
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
978
Classification of Systems-II01:31

Classification of Systems-II

637
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
637
Force Classification01:22

Force Classification

2.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Sparse Non-Local CRF With Applications.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Regularized Loss With Hyperparameter Estimation for Weakly Supervised Single Class Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Transcriptome-Wide Off-Target Effects of Steric-Blocking Oligonucleotides.

Nucleic acid therapeutics·2021
Same author

Image Segmentation Using Deep Learning: A Survey.

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

Efficient Graph Cut Optimization for Full CRFs with Quantized Edges.

IEEE transactions on pattern analysis and machine intelligence·2019
Same author

Constrained-CNN losses for weakly supervised segmentation.

Medical image analysis·2019
Same journal

Color Image Segmentation in a Quaternion Framework.

Energy minimization methods in computer vision and pattern recognition. International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition·2011
See all related articles

Related Experiment Video

Updated: Apr 22, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K

Interactive Segmentation with Super-Labels.

Andrew Delong, Lena Gorelick, Frank R Schmidt

    Energy Minimization Methods in Computer Vision and Pattern Recognition. International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
    |October 14, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel two-level Markov Random Field (MRF) model for image segmentation, addressing limitations of traditional methods by capturing complex object appearances and spatial correlations for improved accuracy.

    More Related Videos

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.6K
    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
    05:56

    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

    Published on: April 14, 2023

    3.8K

    Related Experiment Videos

    Last Updated: Apr 22, 2026

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
    11:38

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

    Published on: August 23, 2017

    9.8K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.6K
    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
    05:56

    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

    Published on: April 14, 2023

    3.8K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Traditional interactive segmentation models often use Gaussian Mixture Models (GMMs) or histograms for appearance and Markov Random Fields (MRFs) for spatial coherence.
    • These methods assume pixels within an object are independent and identically distributed (i.i.d.), which is often untrue due to multi-modal appearances and spatial correlations.

    Purpose of the Study:

    • To propose a novel segmentation energy function capable of modeling complex object appearances.
    • To introduce a "two-level MRF" that models appearance within objects, overcoming the limitations of existing methods.
    • To develop a new algorithm for efficient and effective segmentation using the proposed model.

    Main Methods:

    • A two-level MRF is proposed, featuring "super-labels" at the top level partitioned into "sub-labels" at the bottom.
    • The hierarchical Potts (hPotts) prior is introduced to manage spatial coherence at both levels of the MRF.
    • A novel algorithm combining proposal generation, alpha-expansion, and re-estimation steps (EM-style alternation) is developed.

    Main Results:

    • Experimental results demonstrate significant conceptual and qualitative improvements over existing segmentation methods.
    • The proposed model effectively handles complex appearances and spatial correlations within objects.
    • Applications in binary segmentation, multi-class segmentation, and interactive co-segmentation showcase the model's versatility.

    Conclusions:

    • The developed two-level MRF with hPotts prior offers a more robust approach to interactive image segmentation.
    • The novel algorithm provides an effective solution for optimizing the proposed complex energy function.
    • The framework offers interesting interpretations related to semi-supervised learning, opening avenues for future research.