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

Related Concept Videos

Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

Binomial Probability Distribution

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...

You might also read

Related Articles

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

Sort by

Same author

Modern teaching for clinical education involving manual therapy: a 6-pillar approach.

Chiropractic & manual therapies·2026

Same author

CamIO in the Browser: A Cross-Platform Audio Label Tool for Tactile Graphics.

MobileHCI : proceedings of the ... International Conference on Human Computer Interaction with Mobile Devices and Services. MobileHCI (Conference)·2025

Same author

Supervised Toothbrushing and Silver Diamine Fluoride Application of Rohingya Children in a Refugee Camp in Bangladesh.

International dental journal·2025

Same author

Oral Health of Rohingya Refugees Accessing Emergency Dental Care in the Refugee Camp in Cox's Bazar, Bangladesh.

Community dentistry and oral epidemiology·2025

Same author

Co-Designing Auditory Navigation Solutions for Traveling as a Blind Individual During the COVID-19 Pandemic.

Journal of blindness innovation and research·2024

Same author

Impact of COVID-19 on dental education in Europe: The students' perspective.

European journal of dental education : official journal of the Association for Dental Education in Europe·2021

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

BEST: Barcode Enabled Sequencing of Tetrads

BEST: Barcode Enabled Sequencing of Tetrads

Published on: May 1, 2014

A Bayesian Algorithm for Reading 1D Barcodes.

Ender Tekin¹, James Coughlan

¹The Smith-Kettlewell Eye Research Institute.

Proceedings. Canadian Conference on Computer and Robot Vision

|April 30, 2010

Summary

This summary is machine-generated.

This study introduces a Bayesian framework for accurately reading 1D barcodes from low-quality mobile phone images. The new method improves barcode recognition despite image distortions and noise, outperforming commercial readers.

More Related Videos

Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications

Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications

Published on: April 14, 2015

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Related Experiment Videos

Last Updated: Jun 13, 2026

BEST: Barcode Enabled Sequencing of Tetrads

BEST: Barcode Enabled Sequencing of Tetrads

Published on: May 1, 2014

Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications

Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications

Published on: April 14, 2015

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Computer Vision
Machine Learning
Data Science

Background:

1D barcodes (e.g., UPC) are essential for retail, labeling ~99% of US packaged goods.
Consumer convenience necessitates reading barcodes via portable cameras (e.g., mobile phones).
Low image quality and resolution from portable cameras hinder accurate barcode reading.

Purpose of the Study:

To develop a robust Bayesian framework for reading 1D barcodes from challenging images.
To enhance barcode reading accuracy by modeling geometric distortions and image noise.
To improve upon existing commercial barcode readers.

Main Methods:

A Bayesian framework is proposed, modeling barcode shape and appearance.
The model accounts for geometric distortions and image noise.
Redundant information from the parity digit is exploited; not all barcode edges require detection.

Main Results:

Experiments on a public dataset assessed the range of readable barcode images.
The proposed algorithm demonstrated superior performance compared to two commercial readers.
The framework effectively handles images with geometric distortions and noise.

Conclusions:

The Bayesian framework offers a significant improvement for reading 1D barcodes from low-quality images.
This technology enables more reliable barcode scanning using consumer-grade portable cameras.
The method's robustness to image imperfections makes it suitable for real-world applications.