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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

117
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
117
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

482
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
482
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

418
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
418
Vector Transformation in Rotating Coordinate Systems01:16

Vector Transformation in Rotating Coordinate Systems

1.6K
Consider a vector rotating about an axis with an angular velocity, such that its tip sweeps a circular path.
1.6K
Classification of Signals01:30

Classification of Signals

505
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
505
Classification of Systems-II01:31

Classification of Systems-II

163
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
163

You might also read

Related Articles

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

Sort by
Same author

Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis.

Journal of imaging·2021
Same author

Comparative Study of Data Matrix Codes Localization and Recognition Methods.

Journal of imaging·2021
Same author

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms.

Sensors (Basel, Switzerland)·2021
See all related articles

Related Experiment Video

Updated: Jul 15, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Using Different Types of Artificial Neural Networks to Classify 2D Matrix Codes and Their Rotations-A Comparative

Ladislav Karrach1, Elena Pivarčiová1

  • 1Department of Manufacturing and Automation Technology, Faculty of Technology, Technical University in Zvolen, Masarykova 24, 960 01 Zvolen, Slovakia.

Journal of Imaging
|September 27, 2023
PubMed
Summary

Convolutional neural networks (CNNs) excel at classifying 2D matrix codes, outperforming multilayer perceptrons and radial basis function networks. This study compares four artificial neural networks for robust code recognition.

Keywords:
2D matrix codesclassificationconvolutional neural networkmultilayer perceptronprobabilistic neural networkradial basis function neural network

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks
08:42

Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks

Published on: August 4, 2018

14.1K

Related Experiment Videos

Last Updated: Jul 15, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks
08:42

Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks

Published on: August 4, 2018

14.1K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Artificial neural networks (ANNs) are powerful tools for computer vision tasks like image classification and object detection.
  • Classifying 2D matrix codes (e.g., Data Matrix, QR codes, Aztec codes) and their rotations presents a significant challenge in pattern recognition.

Purpose of the Study:

  • To comparatively evaluate the performance of four ANNs: multilayer perceptrons (MLPs), probabilistic neural networks (PNNs), radial basis function neural networks (RBFNNs), and convolutional neural networks (CNNs).
  • To investigate the ability of these ANNs to accurately classify various 2D matrix codes and their rotational variations.

Main Methods:

  • Detailed explanation of the fundamental components and architectures of MLPs, PNNs, RBFNNs, and CNNs.
  • Comparative analysis of classification accuracy across different ANN configurations using a dataset of 3000 synthetic 2D matrix code samples.
  • Training and testing of each ANN model on the comprehensive dataset to assess performance.

Main Results:

  • When trained on the full dataset, CNNs demonstrated superior performance in classifying 2D matrix codes.
  • RBFNNs achieved the second-best classification accuracy, followed closely by MLPs.
  • The study highlights the effectiveness of specific ANN architectures for 2D code recognition tasks.

Conclusions:

  • Convolutional neural networks are highly effective for the classification of 2D matrix codes, including handling rotational variations.
  • The comparative study provides valuable insights into the strengths of different artificial neural network architectures for barcode recognition.
  • Findings suggest CNNs as a preferred model for robust and accurate 2D matrix code classification in computer vision applications.