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 Experiment Videos

Grayscale level connectivity: theory and applications.

Ulisses Braga-Neto1, John Goutsias

  • 1Section of Clinical Cancer Genetics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. ulisses@ee.tamu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 4, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis.

PloS one·2022
Same author

A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations.

Sensors (Basel, Switzerland)·2022
Same author

Contribution of Coronavirus-Specific Immunoglobulin G Responses to Complement Overactivation in Patients with Severe Coronavirus Disease 2019.

The Journal of infectious diseases·2022
Same author

A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread.

Mathematical biosciences and engineering : MBE·2021
Same author

Estimating DNA methylation potential energy landscapes from nanopore sequencing data.

Scientific reports·2021
Same author

Converging genetic and epigenetic drivers of paediatric acute lymphoblastic leukaemia identified by an information-theoretic analysis.

Nature biomedical engineering·2021
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

A new grayscale level connectivity method defines image connectivity using binary connections on level sets. This approach offers flexibility, allowing connected objects with multiple regional maximums for advanced image analysis.

Area of Science:

  • Computer Vision
  • Image Processing
  • Mathematical Morphology

Background:

  • Existing grayscale connectivity methods have limitations.
  • Some methods require all image-level sets to be connected.
  • Connected grayscale objects are often restricted to a single regional maximum.

Purpose of the Study:

  • Introduce a novel grayscale level connectivity.
  • Provide a flexible framework for image connectivity.
  • Expand the definition of connected grayscale objects.

Main Methods:

  • Defined connectivity via binary connections on image-level sets.
  • Established grayscale level connectivity based on thresholded level sets.
  • Studied connectivity within the framework of connectivity classes.

Related Experiment Videos

Main Results:

  • The proposed grayscale level connectivity includes existing methods as special cases.
  • The framework allows connected grayscale objects with multiple regional maximums.
  • Demonstrated flexibility by not requiring all image-level sets to be connected.

Conclusions:

  • Grayscale level connectivity offers a novel and flexible approach to image connectivity.
  • This method enhances capabilities for object extraction, segmentation, and filtering.
  • The framework supports advanced hierarchical image representation.