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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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,...

You might also read

Related Articles

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

Sort by
Same author

Associations between disturbed sleep and attenuated psychotic experiences in people at clinical high risk for psychosis.

Psychological medicine·2024
Same author

CLEAR - clozapine in early psychosis: study protocol for a multi-centre, randomised controlled trial of clozapine vs other antipsychotics for young people with treatment resistant schizophrenia in real world settings.

BMC psychiatry·2024
Same author

Publisher Correction: Brain charts for the human lifespan.

Nature·2022
Same author

Brain charts for the human lifespan.

Nature·2022
Same author

Establishing the tolerability to broiler chickens and laying hens of nonanoic acid at practical levels of use as a feed flavouring.

British poultry science·2021
Same author

Stress reactivity as a putative mechanism linking childhood trauma with clinical outcomes in individuals at ultra-high-risk for psychosis: Findings from the EU-GEI High Risk Study.

Epidemiology and psychiatric sciences·2021

Related Experiment Video

Updated: Jul 7, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

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

Published on: December 15, 2023

Eigenpaxels and a neural-network approach to image classification.

P McGuire1, G T D'Eleuterio

  • 1C-Core, St. John's, NF A1B 3X5, Canada.

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an eigenpaxel-based image classification method using localized principal components. The novel approach achieves robust performance comparable to existing techniques, even with image noise.

Related Experiment Videos

Last Updated: Jul 7, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

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

Published on: December 15, 2023

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Traditional image classification methods can be computationally intensive.
  • Principal Component Analysis (PCA) is a dimensionality reduction technique.
  • Representing images using global features can be limiting.

Purpose of the Study:

  • To present a novel expansion encoding approach for image classification.
  • To introduce localized principal components, termed "eigenpaxels", as image basis functions.
  • To evaluate the performance and robustness of the eigenpaxel algorithm.

Main Methods:

  • Applying principal-component analysis (PCA) locally to generate "eigenpaxels".
  • Statistically determining eigenpaxels from a database of relevant images.
  • Utilizing expansion encoding and subsampling for image processing.
  • Employing a single-layer error-correcting neural network for classification.

Main Results:

  • The eigenpaxel algorithm demonstrated performance equivalent to existing methods on a frontal face image database.
  • The method proved robust against various forms of image noise.
  • Expansion encoding and subsampling were identified as key processing elements.

Conclusions:

  • The eigenpaxel approach offers an efficient and effective method for image classification.
  • Localized feature extraction via eigenpaxels provides a robust alternative to global methods.
  • This technique shows promise for real-world applications requiring reliable image recognition.