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 Experiment Videos

Neural recognition in a pyramidal structure.

V Cantoni1, A Petrosino

  • 1Dipt. di Inf. e Sistemistica, Pavia Univ.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

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

Offspring burden in Frontotemporal Dementia: the other side of caregiving.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology·2025
Same author

Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis.

NeuroImage. Clinical·2025
Same author

Neurofilament light chain and profilin-1 dynamics in 30 spinal muscular atrophy type 3 patients treated with nusinersen.

European journal of neurology·2024
Same author

Efficacy of transversus abdominis plane (TAP) block in colorectal surgery: a systematic review and meta-analysis.

Techniques in coloproctology·2020
Same author

Modulating risky decision-making in Parkinson's disease by transcranial direct current stimulation.

European journal of neurology·2017
Same author

Reclassification of cardiovascular risk by myocardial perfusion imaging in diabetic patients with abnormal resting electrocardiogram.

Nutrition, metabolism, and cardiovascular diseases : NMCD·2014
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

This study introduces a novel hierarchical system for learning and recognizing objects in images using multiresolution analysis and neural networks. The approach demonstrates effective pattern recognition inspired by biological systems.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biologically Inspired Computing

Background:

  • Biological systems offer advanced pattern recognition capabilities.
  • Existing computational models often lack hierarchical or modular structures for complex image analysis.
  • Learning by example is a key requirement for robust object recognition systems.

Purpose of the Study:

  • To propose a novel hierarchical modular system for pattern recognition.
  • To develop a system capable of learning from examples and recognizing objects in digital images.
  • To evaluate the system's performance and computational efficiency.

Main Methods:

  • Utilizing multiresolution image analysis techniques.
  • Employing neural networks for pattern recognition and learning.

Related Experiment Videos

  • Implementing a hierarchical modular architecture for system design.

    • Main Results:

    • The proposed system demonstrates effective object recognition capabilities on diverse datasets.
    • Experimental timings indicate efficient performance on Single Instruction Multiple Data (SIMD) architectures.
    • The hierarchical modular approach facilitates learning by example.

    Conclusions:

    • The developed system provides a viable biologically inspired solution for pattern recognition.
    • Multiresolution analysis and neural networks are effective components for image analysis.
    • The hierarchical modular structure is a promising approach for advanced AI systems.