Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 2, 2026

Eye Movement Monitoring of Memory

Eye Movement Monitoring of Memory

Published on: August 15, 2010

An Image Statistics-Based Model for Fixation Prediction.

Victoria Yanulevskaya, Jan Bernard Marsman, Frans Cornelissen

Cognitive Computation

|April 9, 2011

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

Contributed Talks III: At a glance - Facilitated identification of abnormal representations in the visual cortex with fMRI micro-probing.

Journal of vision·2025

Same author

Ellipsoid Segmentation Model for Analyzing Light-Attenuated 3D Confocal Image Stacks of Fluorescent Multi-Cellular Spheroids.

PloS one·2016

Same author

Pharmacological characterization of cultivated neuronal networks: relevance to synaptogenesis and synaptic connectivity.

Cellular and molecular neurobiology·2014

Same author

A proto-object-based computational model for visual saliency.

Journal of vision·2013

Same author

Quantitation of chronic and acute treatment effects on neuronal network activity using image and signal analysis: toward a high-content assay.

Journal of biomolecular screening·2013

Same author

Translation of a tumor microenvironment mimicking 3D tumor growth co-culture assay platform to high-content screening.

Journal of biomolecular screening·2012

Same journal

Keying Into Cognition: Temporal Smoothing of Smartphone Typing Behaviors for Passive Assessment of Processing Speed and Executive Function in Individuals With Mood Disorders.

Cognitive computation·2026

Same journal

Neurodynamical Computing at the Information Boundaries of Intelligent Systems.

Cognitive computation·2024

Same journal

Large-Kernel Attention for 3D Medical Image Segmentation.

Cognitive computation·2024

Same journal

Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.

Cognitive computation·2023

Same journal

Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus.

Cognitive computation·2023

Same journal

Deep Learning Based Traffic Prediction Method for Digital Twin Network.

Cognitive computation·2023

See all related articles

Predicting human gaze in images is improved by analyzing image features. A new model using Weibull distribution parameters for contrast and edges offers state-of-the-art performance in salient region detection.

Area of Science:

Computer Vision
Computational Neuroscience
Image Processing

Background:

Salient region detection, predicting human visual attention, is crucial for image understanding.
Low-level image features, particularly contrast and edge information, correlate strongly with human fixation points.
Natural image contrast distributions can be modeled effectively using a two-parameter Weibull distribution.

Purpose of the Study:

To investigate if Weibull distribution parameters can serve as a simple predictive model for human fixation locations in natural images.
To assess the utility of Weibull parameters in distinguishing between fixated and non-fixated image regions.

Main Methods:

Characterizing natural image contrast distributions with a two-parameter Weibull distribution.

More Related Videos

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Related Experiment Videos

Last Updated: Jun 2, 2026

Eye Movement Monitoring of Memory

Eye Movement Monitoring of Memory

Published on: August 15, 2010

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Analyzing the joint distribution of Weibull parameters in fixated versus non-fixated image regions using eye-tracking data.

Developing a classifier based on the log-likelihood ratio of these distributions.

Main Results:

The parameters of the Weibull distribution effectively capture image structures relevant to visual attention.
A simple classifier utilizing only two Weibull parameters per region achieved performance comparable to existing state-of-the-art bottom-up saliency prediction models.
The model demonstrates the predictive power of statistical image features for human visual behavior.

Conclusions:

The Weibull distribution parameters provide a computationally efficient and effective method for salient region detection.
This approach offers a simplified yet powerful model for predicting human visual attention in natural scenes.
The findings contribute to a better understanding of the relationship between image statistics and visual perception.