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

Ogive Graph01:07

Ogive Graph

6.8K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.8K
Graphing Antiderivatives01:30

Graphing Antiderivatives

69
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
69
Bar Graph01:07

Bar Graph

22.1K
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...
22.1K
Graphs of Functions01:30

Graphs of Functions

331
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
331
Time-Series Graph00:54

Time-Series Graph

5.1K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.1K
Multiple Bar Graph01:07

Multiple Bar Graph

9.3K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Fluid dynamics reduction methods for temporal networks.

Scientific reports·2026
Same author

NAc-DBS selectively enhances memory updating without effect on retrieval.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2025
Same author

Characterizing the dynamics of unlabeled temporal networks.

Chaos (Woodbury, N.Y.)·2025
Same author

Proteogenomic discovery of <i>RB1</i>-defective phenocopy in cancer predicts disease outcome, response to treatment, and therapeutic targets.

Science advances·2025
Same author

The transcriptomic architecture of common cancers reflects synthetic lethal interactions.

Nature genetics·2025
Same author

On the structure of species-function participation in multilayer ecological networks.

Nature communications·2024
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jan 30, 2026

Bringing the Visible Universe into Focus with Robo-AO
10:35

Bringing the Visible Universe into Focus with Robo-AO

Published on: February 12, 2013

20.1K

Visibility Graphs for Image Processing.

Jacopo Iacovacci, Lucas Lacasa

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 11, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Image visibility graphs (IVGs) map image data to graphs, revealing structural information. These novel graph features offer efficient and versatile tools for image classification and pattern recognition.

    More Related Videos

    Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment
    07:02

    Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment

    Published on: June 30, 2023

    2.2K
    Culturing, Freezing, Processing, and Imaging of Entire Organoids and Spheroids While Still in a Hydrogel
    08:07

    Culturing, Freezing, Processing, and Imaging of Entire Organoids and Spheroids While Still in a Hydrogel

    Published on: December 23, 2022

    8.1K

    Related Experiment Videos

    Last Updated: Jan 30, 2026

    Bringing the Visible Universe into Focus with Robo-AO
    10:35

    Bringing the Visible Universe into Focus with Robo-AO

    Published on: February 12, 2013

    20.1K
    Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment
    07:02

    Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment

    Published on: June 30, 2023

    2.2K
    Culturing, Freezing, Processing, and Imaging of Entire Organoids and Spheroids While Still in a Hydrogel
    08:07

    Culturing, Freezing, Processing, and Imaging of Entire Organoids and Spheroids While Still in a Hydrogel

    Published on: December 23, 2022

    8.1K

    Area of Science:

    • Graph theory
    • Image processing
    • Computer vision

    Background:

    • Image visibility graphs (IVGs) are novel algorithms for mapping scalar fields to graph structures.
    • Their potential in image processing and classification remains largely unexplored.

    Purpose of the Study:

    • To investigate the utility of image visibility graphs (IVGs) in image processing and classification.
    • To demonstrate that IVG link architecture captures essential image structural information.

    Main Methods:

    • Mapping scalar fields to graph structures using IVG algorithms.
    • Analyzing the link architecture of IVGs to extract image features.
    • Introducing and evaluating 'Visibility Patches' as a novel graph feature.

    Main Results:

    • IVG link architecture effectively encapsulates image structural information.
    • Introduced graph features, including Visibility Patches, are highly informative.
    • These features demonstrate computational efficiency and universal applicability.

    Conclusions:

    • Image visibility graphs offer a powerful new approach for image analysis.
    • The proposed graph features are promising for general pattern recognition and image classification tasks.