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

Region-level motion-based background modeling and subtraction using MRFs.

Shih-Shinh Huang1, Li-Chen Fu, Pei-Yung Hsiao

  • 1Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 12, 2007
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

Multimodal Depression Detection Through Conversational Interactions with an Emotion-Aware Social Robot: Pilot Study.

JMIR formative research·2026
Same author

Mild Cognitive Impairment Detection System Based on Unstructured Spontaneous Speech: Longitudinal Dual-Modal Framework.

JMIR medical informatics·2026
Same author

Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.

BioData mining·2025
Same author

Self-Supervised Guided Modality Disentangled Representation Learning for Multimodal Sentiment Analysis and Schizophrenia Assessment.

IEEE journal of biomedical and health informatics·2025
Same author

Response to comment on "The effect of electroacupuncture merged with rehabilitation for frozen shoulder syndrome: A single-blind randomized sham-acupuncture controlled study" [1].

Journal of the Formosan Medical Association = Taiwan yi zhi·2025
Same author

Deep Learning-Based Instance Appraisable Model (EDi Pain) for Pain Estimation via Facial Videos: A Retrospective Analysis and a Prospective Emergency Department Study.

Journal of imaging informatics in medicine·2025
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

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

This study introduces a novel method for automatic foreground object segmentation in image sequences. By integrating background subtraction and motion analysis, it enhances foreground detection accuracy in videos.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Accurate foreground object segmentation is crucial for video analysis.
  • Existing methods often struggle with complex background changes and motion ambiguities.

Purpose of the Study:

  • To develop an improved automatic segmentation approach for foreground objects in image sequences.
  • To enhance the accuracy and robustness of foreground detection by integrating diverse segmentation techniques.

Main Methods:

  • A region-based motion segmentation algorithm identifies motion-coherent regions and their temporal correspondences.
  • A graph labeling approach using Markov Random Fields (MRFs) models the segmentation problem.
  • The MRF model incorporates likelihood energy from a background model and prior energy for spatial and temporal coherence.

Related Experiment Videos

Main Results:

  • The proposed method effectively segments foreground objects from image sequences.
  • Experimental results demonstrate improved accuracy compared to traditional methods.
  • The integration of background subtraction and motion-based segmentation leads to more robust performance.

Conclusions:

  • The novel approach offers a more accurate and reliable method for automatic foreground segmentation.
  • The MRF-based framework elegantly combines spatial and temporal information for enhanced segmentation continuity.
  • This technique shows significant potential for various video analysis applications.