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

A computational model for visual selection.

Y Amit1, D Geman

  • 1Department of Statistics, University of Chicago, Chicago, IL 60637, USA.

Neural Computation
|September 22, 1999
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

Safety and efficacy of turoctocog alfa (NovoEight®) during surgery in patients with haemophilia A: results from the multinational guardian™ clinical trials.

Haemophilia : the official journal of the World Federation of Hemophilia·2014
Same author

Bayes smoothing algorithms for segmentation of binary images modeled by markov random fields.

IEEE transactions on pattern analysis and machine intelligence·2012
Same author

Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.

IEEE transactions on pattern analysis and machine intelligence·2012
Same author

Yttrium synoviorthesis of the elbow joints in persons with haemophilia.

Haemophilia : the official journal of the World Federation of Hemophilia·2004
Same author

Bone allograft in revision total knee replacement.

Cell and tissue banking·2004
Same author

Synoviorthesis with radioactive Yttrium in haemophilia: Israel experience.

Haemophilia : the official journal of the World Federation of Hemophilia·2001
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

This study introduces a computational model for object detection in images. It efficiently identifies potential object regions using a bottom-up approach, minimizing computation and missed detections for tasks like face detection.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Object detection and localization are crucial tasks in computer vision.
  • Existing methods often require intensive computation and can miss detections.
  • Understanding how the brain processes visual information can inspire new algorithms.

Purpose of the Study:

  • To develop a computational model for detecting and localizing object instances in static grayscale images.
  • To focus on efficient visual selection, reducing the number of candidate regions for further processing.
  • To minimize computational cost and missed detections during the object detection process.

Main Methods:

  • A bottom-up processing approach based on local groupings of edge fragments.
  • Geometrical constraints are used to group edge fragments without a priori semantic interpretation.

Related Experiment Videos

  • Training selects specific groupings that are likely on objects but not in the background.

    • Main Results:

    • The model successfully identifies candidate regions containing global arrangements of local groupings.
    • Statistics of object-specific and background groupings are shown to be stable.
    • The algorithm demonstrates effectiveness when applied to face and symbol detection tasks.

    Conclusions:

    • The proposed model offers an efficient method for object detection and localization.
    • The approach aligns with neuroscientific evidence regarding visual processing in areas V1, V2, and IT.
    • This model provides a foundation for more sophisticated object recognition systems.