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

Guided-MLESAC: faster image transform estimation by using matching priors.

Ben J Tordoff1, David W Murray

  • 1Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK. bjt21@cam.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 22, 2005
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

Patellar resurfacing in total knee replacement: 20-year clinical and economic results of a large multicentre, randomised controlled trial in the UK.

Lancet (London, England)·2026
Same author

A Comparison of the Outcomes of Simultaneous Bilateral and Unilateral Mobile-Bearing Unicompartmental Knee Arthroplasties: An Analysis of Data from the National Joint Registry of England, Northern Ireland and Isle of Man.

JB & JS open access·2026
Same author

Assessing clinical and cost effectiveness of total versus partial knee replacement (TOPKAT): 10-year follow-up of a multicentre, randomised controlled trial.

The Lancet. Rheumatology·2025
Same author

Mid-term outcomes of the fixed-bearing lateral Oxford unicompartmental knee arthroplasty.

The bone & joint journal·2025
Same author

Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs.

The Knee·2024
Same author

A Matched Comparison of Implant and Functional Outcomes of Cemented and Cementless Unicompartmental Knee Replacements: A Study from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man and the Hospital Episode Statistics Patient Reported Outcome Measures Database.

The Journal of bone and joint surgery. American volume·2024
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Maximum-likelihood estimation by random sampling consensus (MLESAC) can be improved by estimating prior probabilities of feature correspondences. Guided-MLESAC with these priors offers significant speed increases and enables real-time video processing.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Maximum Likelihood Estimation by Random Sampling Consensus (MLESAC) is a key algorithm for multiview geometry estimation.
  • A limitation of MLESAC is its assumption of unknown prior probabilities for feature correspondences.
  • This prior ignorance impacts both theoretical robustness and practical efficiency.

Purpose of the Study:

  • To enhance the theoretical foundation and practical performance of MLESAC.
  • To introduce a guided approach by incorporating prior probabilities of correspondence validity.
  • To adapt the algorithm for real-time video-rate applications.

Main Methods:

  • Deriving estimates for prior probabilities of feature correspondence validity.
  • Implementing guided-MLESAC using these derived priors.

Related Experiment Videos

  • Introducing two modifications: incorporating all putative matches and propagating frame-to-frame information.

    • Main Results:

    • Guided-MLESAC with priors achieves an order of magnitude speedup for single-transformation problems with clutter.
    • Incorporating all putative matches enhances robustness at a computational cost.
    • Frame-to-frame propagation enables video-rate performance for two-transformation models.

    Conclusions:

    • Estimating prior probabilities significantly improves MLESAC.
    • Guided-MLESAC offers substantial performance gains, particularly for real-world computer vision tasks.
    • The proposed modifications extend MLESAC's applicability to dynamic scenes and complex transformations.