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: Jul 7, 2026

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

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

Clustering in image space for place recognition and visual annotations for human-robot interaction.

A M Martinez¹, J Vitria

¹Robot Vision Lab., Purdue Univ., West Lafayette, IN.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This study introduces an annotation-based system for robot navigation, simplifying user commands. A genetic expectation-maximization algorithm enhances mixture models for improved vision-guided robot navigation.

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

Intravenous Glutathione and Vitamin Supplementation Causing Stevens-Johnson Syndrome: A Case Report.

Journal of burn care & research : official publication of the American Burn Association·2025

Same author

The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images.

Osteoarthritis and cartilage·2022

Same author

Plastisphere on microplastics: In situ assays in an estuarine environment.

Journal of hazardous materials·2022

Same author

[Paediatric Basic and Advanced Life Support].

Notfall & rettungsmedizin·2020

Same author

Trends in depression prevalence in the USA from 2005 to 2015: widening disparities in vulnerable groups.

Psychological medicine·2017

Same author

Stress-related hormonal alterations, growth and pelleted starter intake in pre-weaning Holstein calves in response to thermal stress.

International journal of biometeorology·2017

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Traditional model-based robot navigation requires precise 3-D environment models, complicating user interaction.
Existing methods like PCA and FDA struggle with nonlinear subspaces.

Purpose of the Study:

To develop a more intuitive user-robot interaction for vision-guided navigation.
To propose an appearance-based navigation approach using mixture models.

Main Methods:

Utilizing "annotations" on a descriptive map for simplified user commands (e.g., "go to label 5").
Employing mixture models within an appearance-based paradigm for navigation.
Addressing the local maxima problem in mixture model learning with a genetic expectation-maximization algorithm.

Related Experiment Videos

Last Updated: Jul 7, 2026

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

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

Main Results:

Demonstrated successful vision-guided mobile robot navigation using the annotation system.
Showcased the effectiveness of mixture models in representing nonlinear subspaces for navigation.
Validated the performance improvement achieved by the genetic expectation-maximization algorithm.

Conclusions:

Annotation-based interaction significantly simplifies user commands for robot navigation.
Mixture models, enhanced by a genetic EM algorithm, offer a robust solution for appearance-based robot navigation.
The proposed approach enhances the practicality and usability of autonomous robot navigation systems.