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

Histogram

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...

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.

Relative Frequency Histogram

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...

Bar Graph

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...

Gestalt Principles of Perception

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...

You might also read

Related Articles

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

Sort by

Same author

Chromosome number and ploidy level of balm (Melissa officinalis).

Molecular cytogenetics·2015

Same author

The probability distribution of conditional classification error.

IEEE transactions on pattern analysis and machine intelligence·2011

Same author

Statistical properties of error estimators in performance assessment of recognition systems.

IEEE transactions on pattern analysis and machine intelligence·2011

Same author

The adaptive hough transform.

IEEE transactions on pattern analysis and machine intelligence·2011

Same author

A Unified Framework for Biometric Expert Fusion Incorporating Quality Measures.

IEEE transactions on pattern analysis and machine intelligence·2011

Same author

Region growing: a new approach.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Histogram-based segmentation in a perceptually uniform color space.

L Shafarenko, H Petrou, J Kittler

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 16, 2008

Summary

This summary is machine-generated.

This study introduces a color image segmentation algorithm using the watershed algorithm on smoothed color histograms. The method ensures accurate segmentation in perceptually uniform color spaces like Luv to prevent oversegmentation.

More Related Videos

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

EasyFiji: A Graphical Interface for User-Friendly Fluorescence Image Processing in Fiji

EasyFiji: A Graphical Interface for User-Friendly Fluorescence Image Processing in Fiji

Published on: February 20, 2026

Related Experiment Videos

Last Updated: Jul 7, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

EasyFiji: A Graphical Interface for User-Friendly Fluorescence Image Processing in Fiji

EasyFiji: A Graphical Interface for User-Friendly Fluorescence Image Processing in Fiji

Published on: February 20, 2026

Area of Science:

Computer Vision
Image Processing
Color Science

Background:

Color image segmentation is crucial for image analysis.
Existing methods may suffer from oversegmentation.
Perceptually uniform color spaces are vital for human-like color perception.

Discussion:

The proposed algorithm utilizes the watershed algorithm for image segmentation.
It operates on either two-dimensional (2-D) or three-dimensional (3-D) color histograms.
Segmentation is performed in perceptually uniform color spaces, specifically Luv space, for accurate color representation.

Key Insights:

Applying the watershed algorithm to a smoothed histogram effectively mitigates oversegmentation.
The use of Luv color space ensures segmentation aligns with human color perception.
This approach offers a robust method for color image segmentation.

Outlook:

Potential for application in various image analysis tasks.
Further research could explore other color spaces or smoothing techniques.
Optimization for real-time processing could enhance its utility.