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 Video

Updated: May 26, 2026

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

Interactive image segmentation using Dirichlet process multiple-view learning.

Lei Ding1, Alper Yilmaz, Rong Yan

  • 1Intent Media Inc., New York, NY 10014, USA. leiding326@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 29, 2011
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

Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases.

Briefings in bioinformatics·2025
Same author

Optimizing Multilayer Networks Through Time-Dependent Decision-Making: A Comparative Study.

Big data·2025
Same author

The role of ceRNAs in breast cancer microenvironmental regulation and therapeutic implications.

Journal of molecular medicine (Berlin, Germany)·2024
Same author

Geometric Wide-Angle Camera Calibration: A Review and Comparative Study.

Sensors (Basel, Switzerland)·2024
Same author

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

Interdisciplinary sciences, computational life sciences·2024
Same author

Carcass characteristics and meat quality of goat kids according to the Colomer - Rocher carcass fatness and conformation classes.

Meat science·2024
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

This study introduces an interactive image segmentation framework using seed pixels to identify objects. The method effectively segments whole objects even with limited initial data.

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Image segmentation is complex, especially for whole objects without common sense reasoning.
  • Existing methods struggle with limited initial object and background information.

Purpose of the Study:

  • To present an interactive segmentation framework integrating appearance and boundary constraints.
  • To enable accurate whole object segmentation from images with minimal seed pixels.

Main Methods:

  • Utilized seed pixels for object and background labeling.
  • Employed Dirichlet process multiple-view learning for label estimation.
  • Integrated appearance and boundary constraints via multiple-view learning.
  • Used Dirichlet process mixture-based nonlinear classification for feature modeling and discrimination.

Related Experiment Videos

Last Updated: May 26, 2026

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

Main Results:

  • Achieved quantitatively and qualitatively promising results on a standard image dataset.
  • Demonstrated effective whole object segmentation with insufficient seed pixels.
  • The framework successfully integrates image appearance and boundary information.

Conclusions:

  • The proposed interactive framework offers a principled approach to image segmentation.
  • It effectively handles challenges posed by limited initial labeling.
  • The method shows significant potential for accurate whole object segmentation.