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

Related Concept Videos

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

Gestalt Principles of Perception

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...

Perceptual Constancy

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...

You might also read

Related Articles

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

Sort by

Same author

Rapid Artificial Intelligence Autoplanning Rivals Manual Expert Planning for Cervical Brachytherapy.

Practical radiation oncology·2026

Same author

Macula Spatial Patterns and Their Association With Central Visual Field Progression in Glaucoma Using Artificial Intelligence.

Journal of glaucoma·2026

Same author

Integration of single-click, AI-based brachytherapy auto-planning for cervical cancer within a treatment planning system.

Brachytherapy·2025

Same author

Deep Learning Estimation of 24-2 Visual Field Map From Optic Nerve Head Optical Coherence Tomography Angiography.

Journal of glaucoma·2025

Same author

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

Medical physics·2024

Same author

Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements.

American journal of ophthalmology·2023

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

Search research articles

Related Experiment Video

Updated: Jun 18, 2026

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

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

Published on: December 15, 2023

Spatiotemporal saliency in dynamic scenes.

Vijay Mahadevan¹, Nuno Vasconcelos

¹Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093-0407, USA. vmahadev@ucsd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|November 21, 2009

Summary

This summary is machine-generated.

This study introduces a novel spatiotemporal saliency algorithm using dynamic textures for robust visual attention detection. The algorithm significantly improves background subtraction performance, achieving nearly half the error rate of competitors.

More Related Videos

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

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

Published on: January 23, 2017

Related Experiment Videos

Last Updated: Jun 18, 2026

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

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

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

Published on: January 23, 2017

Area of Science:

Computer Vision
Computational Neuroscience

Background:

Existing saliency models often struggle with dynamic scenes and moving cameras.
Biological motion perception provides inspiration for enhanced visual attention mechanisms.

Purpose of the Study:

To propose a spatiotemporal saliency algorithm inspired by biological motion perception.
To develop a robust and unsupervised method for visual attention detection in complex dynamic environments.
To improve background subtraction accuracy using saliency principles.

Main Methods:

Developed a center-surround saliency framework extending discriminant formulations for static images.
Modeled spatiotemporal video patches as dynamic textures for joint spatial-temporal feature characterization.
Treated background subtraction as the complement of saliency detection by classifying non-salient regions.

Main Results:

The proposed algorithm demonstrates robustness in scenes with highly dynamic backgrounds and moving cameras.
Achieved substantial performance improvements in background subtraction compared to state-of-the-art techniques.
Reported an average error rate nearly half that of the closest competitor in background subtraction tasks.

Conclusions:

The integration of discriminant center-surround saliency and dynamic texture modeling offers a versatile and unsupervised approach.
The algorithm effectively detects spatiotemporal saliency and enhances background subtraction.
This method shows significant potential for real-world applications involving dynamic visual scene analysis.