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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.
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Histogram01:05

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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).
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Relative Frequency Histogram01:14

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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...
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Related Experiment Video

Updated: Jan 30, 2026

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis
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Fast auto-focusing search algorithm for a high-speed and high-resolution camera based on the image histogram feature

Chenzi Guo, Zelong Ma, Xu Guo

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    |January 16, 2019
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    This study introduces a new auto-focus image quality evaluation function and a fast auto-focus search algorithm for high-speed cameras. The new method offers improved efficiency and accuracy over traditional approaches.

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    Area of Science:

    • Optics and photonics
    • Image processing
    • Computational imaging

    Background:

    • High-speed cameras require efficient and accurate auto-focusing mechanisms.
    • Traditional auto-focus methods can be computationally intensive and less precise.
    • Image quality evaluation is crucial for effective focusing.

    Purpose of the Study:

    • To develop a novel auto-focus image quality evaluation function for high-speed cameras.
    • To propose a fast auto-focus search algorithm based on the new evaluation function.
    • To compare the proposed method's performance against traditional techniques.

    Main Methods:

    • Developed an auto-focus image quality evaluation function using the histogram feature function (HFF).
    • Designed a fast auto-focus search algorithm leveraging the new evaluation function.
    • Evaluated computational resources, focusing area effectiveness, and sensitivity.
    • Compared performance against traditional climbing methods and evaluation functions.

    Main Results:

    • The proposed evaluation function requires fewer computational resources than traditional methods.
    • The new function provides a more effective focusing area and moderate sensitivity.
    • The fast auto-focus search algorithm achieves focus faster, reducing motor movements.
    • Improved focusing accuracy and reduced focusing time were demonstrated.

    Conclusions:

    • The developed auto-focus image quality evaluation function and fast search algorithm offer significant advantages for high-speed cameras.
    • The new approach enhances focusing speed, accuracy, and computational efficiency.
    • This method represents a notable advancement in auto-focus technology for demanding imaging applications.