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 Concept Videos

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

You might also read

Related Articles

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

Sort by
Same author

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

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

Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Demonstration of efficient predictive surrogates for large-scale quantum processors.

Nature communications·2026
Same author

A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice.

Nature communications·2026
Same author

NoisePO: Efficient Semantic Noise Generation and Ranking for Diffusion-Based Text-to-Image Synthesis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Stability and Generalization for Distributed SGDA.

IEEE transactions on pattern analysis and machine intelligence·2026

Related Experiment Video

Updated: Jun 16, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Biologically inspired features for scene classification in video surveillance.

Kaiqi Huang1, Dacheng Tao, Yuan Yuan

  • 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 27, 2010
PubMed
Summary

This study introduces a novel scene classification method inspired by human vision, offering improved robustness and selectivity for video surveillance. The new approach addresses challenges like occlusion and disorder with lower complexity.

Related Experiment Videos

Last Updated: Jun 16, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Scene classification is crucial for understanding visual data.
  • Existing methods face challenges with robustness, selectivity, and complexity.
  • Human visual cognition offers potential for improved algorithms.

Purpose of the Study:

  • To develop a novel scene classification method inspired by human visual cognition.
  • To enhance robustness, selectivity, and reduce complexity in scene classification.
  • To address occlusion and disorder issues in video surveillance scene classification.

Main Methods:

  • Utilized an improved standard model feature inspired by human visual cognition.
  • Conducted experiments on a custom database and standard public datasets.
  • Investigated occlusion and disorder problems in video surveillance context.

Main Results:

  • The proposed method demonstrated superior robustness and selectivity compared to state-of-the-art approaches.
  • Experimental results validated the method's effectiveness on diverse datasets.
  • Achieved lower computational complexity than existing scene classification techniques.

Conclusions:

  • The novel scene classification method offers significant advantages for computer vision tasks.
  • The approach shows promise for real-world applications, particularly in video surveillance.
  • Further research into human visual mechanisms can yield more advanced AI systems.