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Related Concept Videos

Pie Chart01:04

Pie Chart

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A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
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Interpreting R Charts01:22

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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Time-Series Graph00:54

Time-Series Graph

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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...
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Bar Graph01:07

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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...
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Interpreting X̄ Charts01:13

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Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
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Pareto Chart00:52

Pareto Chart

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A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
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Chartem: Reviving Chart Images with Data Embedding.

Jiayun Fu, Bin Zhu, Weiwei Cui

    IEEE Transactions on Visualization and Computer Graphics
    |December 14, 2020
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    Summary
    This summary is machine-generated.

    Chartem embeds data into chart images, allowing reuse without human perception interference. This novel approach improves chart data extraction and repurposing, overcoming limitations of current computer vision methods.

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    Area of Science:

    • Computer Vision
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Charts are commonly stored as static bitmap images, hindering data manipulation and reuse.
    • Existing methods for extracting information from chart images face challenges in robustness and accuracy.

    Purpose of the Study:

    • To introduce Chartem, a novel approach for embedding data within chart images.
    • To enable the reuse and repurposing of chart images through embedded data.

    Main Methods:

    • Development of a data-embedding schema to encode information into the background of chart images.
    • Design of algorithms for embedding and extracting data without affecting human perception.
    • Evaluation through user studies and performance experiments.

    Main Results:

    • Chartem successfully embeds significant information into chart images.
    • Embedded data can be extracted to enable various visualization applications.
    • User studies and experiments validate the effectiveness of Chartem's embedding and extraction algorithms.

    Conclusions:

    • Chartem offers a robust and accurate solution for making chart images reusable.
    • The approach addresses the limitations of traditional chart image storage and manipulation.
    • Demonstrated utility through prototype applications highlights Chartem's practical value.