Jove
Visualize
Contact Us

Related Concept Videos

Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Electrodermal activity as an index of food neophobia outside the lab.

Frontiers in neuroergonomics·2024
Same author

Comparing Explicit and Implicit Measures for Assessing Cross-Cultural Food Experience.

Frontiers in neuroergonomics·2024
Same author

Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.

Sensors (Basel, Switzerland)·2023
Same author

Linking Categorical and Dimensional Approaches to Assess Food-Related Emotions.

Foods (Basel, Switzerland)·2022
Same author

Connected Through Mediated Social Touch: "<i>Better Than a Like on Facebook</i>." A Longitudinal Explorative Field Study Among Geographically Separated Romantic Couples.

Frontiers in psychology·2022
Same author

Serial Dependence of Emotion Within and Between Stimulus Sensory Modalities.

Multisensory research·2021
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
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles
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: Jun 3, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Computational versus psychophysical bottom-up image saliency: a comparative evaluation study.

Alexander Toet1

  • 1Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands. lex.toet@tno.nl

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 23, 2011
PubMed
Summary
This summary is machine-generated.

Computational saliency models predict human visual attention. A new Multiscale Contrast Conspicuity (MCC) metric and a simple contrast model best correlate with human visual conspicuity, outperforming other models.

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 3, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Visual perception research
  • Computational modeling
  • Image processing

Background:

  • Local image saliency influences human interpretation of visual scenes.
  • Computational saliency models have diverse applications in image and video processing.
  • Existing models lack crucial visual effects like crowding and lateral interaction.

Purpose of the Study:

  • To compare the predictive accuracy of 13 computational bottom-up saliency models against human visual conspicuity measurements.
  • To evaluate a newly introduced Multiscale Contrast Conspicuity (MCC) metric.
  • To identify models that best correlate with human visual target conspicuity.

Main Methods:

  • Quantifying agreement using rank order correlation between model predictions and human measurements.
  • Comparing 13 computational saliency models and the MCC metric.
  • Utilizing a psychophysical saliency measurement procedure for the MCC metric.

Main Results:

  • The maximum saliency value over the target area correlated most strongly with visual conspicuity for 12 of 13 models.
  • A simple multiscale contrast model and the MCC metric achieved the highest correlation with human visual target conspicuity (r > 0.84).

Conclusions:

  • The MCC metric and a simple multiscale contrast model show strong agreement with human visual conspicuity.
  • Further research into visual mechanisms is needed to improve computational saliency models.
  • The MCC metric is a valuable tool for investigating visual target saliency.