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

Motion field modeling for video sequences.

R Rajagopalan1, M T Orchard, R D Brandt

  • 1IBM Thomas J. Watson Res. Center, Yorktown Heights, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
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

Observation of Cabibbo-Suppressed Two-Body Hadronic Decays and Precision Mass Measurement of the Ω_{c}^{0} Baryon.

Physical review letters·2024
Same author

Measurement of CP Violation in B^{0}→ψ(→ℓ^{+}ℓ^{-})K_{S}^{0}(→π^{+}π^{-}) Decays.

Physical review letters·2024
Same author

Observation of New Baryons in the Ξ_{b}^{-}π^{+}π^{-} and Ξ_{b}^{0}π^{+}π^{-} Systems.

Physical review letters·2023
Same author

Precision Measurement of CP Violation in the Penguin-Mediated Decay B_{s}^{0}→ϕϕ.

Physical review letters·2023
Same author

Measurement of the Λ_{b}^{0}→Λ(1520)μ^{+}μ^{-} Differential Branching Fraction.

Physical review letters·2023
Same author

Observation of New Ω_{c}^{0} States Decaying to the Ξ_{c}^{+}K^{-} Final State.

Physical review letters·2023
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

We developed a novel statistical signal processing model for image motion fields. This approach enhances interframe prediction accuracy by 29% compared to existing methods.

Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Signal Processing

Background:

  • Interframe prediction relies on modeling pixel correspondences (motion fields).
  • Traditional methods often lack a strong statistical foundation.
  • Autoregressive (AR) modeling is common for time-series data.

Purpose of the Study:

  • To propose a novel motion field model based on statistical signal processing.
  • To develop a joint spatio-temporal model incorporating pixel intensities.
  • To enable the application of signal processing tools to motion field analysis.

Main Methods:

  • Generalization of autoregressive (AR) modeling for spatial neighborhoods of motion elements.
  • Integration of spatial pixel intensity neighborhoods into the motion model.

Related Experiment Videos

  • Utilizing statistical signal processing concepts for motion modeling.

    • Main Results:

    • The proposed motion model achieved a 29% improvement in mean squared error energy for interframe prediction over a pel-recursive approach.
    • The joint spatio-temporal model further enhanced prediction efficiency by 8% compared to the motion model alone.
    • Simulations demonstrated the model's capability to estimate optical flow fields.

    Conclusions:

    • The statistical signal processing approach offers a robust framework for motion field modeling.
    • The proposed models significantly improve interframe prediction accuracy.
    • This work facilitates the extension of signal processing techniques for motion analysis and optical flow estimation.