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

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

443
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
443

You might also read

Related Articles

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

Sort by
Same author

Sequence intrinsic somatic mutation mechanisms contribute to affinity maturation of VRC01-class HIV-1 broadly neutralizing antibodies.

Proceedings of the National Academy of Sciences of the United States of America·2017
Same author

Clinicopathological features, management and outcome of patients with poorly-differentiated oral and oropharyngeal squamous cell carcinoma.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery·2017
Same author

Detection of Methanol with Fast Response by Monodispersed Indium Tungsten Oxide Ellipsoidal Nanospheres.

ACS sensors·2017
Same author

Randomized, controlled trial evaluating the effect of multi-strain probiotic on the mucosal microbiota in canine idiopathic inflammatory bowel disease.

Gut microbes·2017
Same author

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.

Biomedical optics express·2017
Same author

Sulfation of the Extracellular Polysaccharide Produced by the King Oyster Culinary-Medicinal Mushroom, Pleurotus eryngii (Agaricomycetes), and Its Antioxidant Properties In Vitro.

International journal of medicinal mushrooms·2017
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026
Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Mar 18, 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

9.7K

A Semi-Supervised Method for Surveillance-Based Visual Location Recognition.

Pengcheng Liu, Peipei Yang, Chong Wang

    IEEE Transactions on Cybernetics
    |June 29, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study addresses cross-device visual location recognition challenges between mobile phones and surveillance cameras. A novel method combining subspace alignment and Laplacian SVM improves accuracy under varying environmental conditions.

    More Related Videos

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    8.3K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.7K

    Related Experiment Videos

    Last Updated: Mar 18, 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

    9.7K
    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    8.3K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.7K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Recognition

    Background:

    • Visual location recognition is crucial for surveillance and mobile applications.
    • Existing methods struggle with images from different device types (mobile vs. surveillance cameras).
    • Environmental variations (time, weather) further complicate cross-device recognition.

    Purpose of the Study:

    • To develop a robust visual location recognition method for cross-device scenarios.
    • To investigate the impact of environmental factors on recognition accuracy.
    • To create a diverse dataset for evaluating cross-device location recognition.

    Main Methods:

    • A novel method unifying unsupervised subspace alignment and semi-supervised Laplacian support vector machine (SVM) was designed.
    • A cross-device location recognition dataset was built, featuring images from mobile phones and surveillance cameras across 22 locations.
    • Experiments were conducted to evaluate the proposed method against existing approaches.

    Main Results:

    • The proposed method demonstrated superior efficiency in cross-device visual location recognition compared to several related methods.
    • The study analyzed the influence of features, time, and weather on recognition performance.
    • The developed dataset facilitated comprehensive evaluation under diverse conditions.

    Conclusions:

    • The unified subspace alignment and Laplacian SVM approach effectively addresses cross-device visual location recognition.
    • Environmental variations significantly impact recognition accuracy, highlighting the need for robust methods.
    • The study provides a valuable dataset and a promising solution for real-world surveillance and mobile applications.