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Related Concept Videos

Histogram01:05

Histogram

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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).
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...
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Relative Frequency Histogram01:14

Relative Frequency Histogram

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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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Probability Histograms01:17

Probability Histograms

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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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Fast representation based on a double orientation histogram for local image descriptors.

Wenxiong Kang, Xiaopeng Chen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 22, 2015
    PubMed
    Summary

    A new local image descriptor, fast representation using a double orientation histogram (FRDOH), enhances feature distinctiveness. Experiments show FRDOH surpasses existing methods in comprehensive performance for image analysis tasks.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Pattern Recognition

    Background:

    • Extensive research exists on local invariant features and descriptors.
    • Existing descriptors offer specific advantages but lack comprehensive performance.
    • Need for a descriptor with improved stability, precision, and speed.

    Purpose of the Study:

    • To develop a novel local image descriptor named fast representation using a double orientation histogram (FRDOH).
    • To enhance the discriminability and comprehensive performance of local image descriptors.
    • To reduce computation time while maintaining high precision in feature description.

    Main Methods:

    • Developed FRDOH descriptor based on existing methods.
    • Utilized intensity order for spatial information encoding.
    • Employed a double orientation histogram for enhanced discriminability.
    • Applied Hellinger distance for histogram bin balancing.
    • Introduced rapidly cascaded interpolation for efficient intensity calculation.

    Main Results:

    • FRDOH descriptor demonstrated superior comprehensive performance.
    • Outperformed state-of-the-art descriptors in experimental evaluations.
    • Achieved high precision with reduced computation time.

    Conclusions:

    • The FRDOH descriptor offers significant improvements over existing methods.
    • FRDOH provides a robust and efficient solution for local feature description.
    • The developed descriptor shows promise for various image analysis applications.