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Related Concept Videos

Histogram01:05

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...

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Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
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Published on: April 7, 2014

Optical-digital method of local histogram calculation by threshold decomposition.

V Kober, T Cichocki, M Gedziorowski

    Applied Optics
    |August 31, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel theorem for calculating local histograms in grayscale images using binary image slicing and convolution. This method enables optical implementation for advanced image processing and nonlinear filtration.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Optical Computing

    Background:

    • Local histograms are crucial for image analysis.
    • Existing methods for calculating local histograms can be computationally intensive.
    • Optical implementations offer potential for high-speed processing.

    Purpose of the Study:

    • To present and prove a new theorem for calculating local histograms of grayscale images.
    • To demonstrate the optical implementation of this calculation.
    • To explore the application of local histograms in nonlinear filtration.

    Main Methods:

    • A theorem based on the convolution of input-image binary slices with a binary kernel.
    • Optical implementation using a shadow-casting correlator.
    • Utilizing local histograms for nonlinear filtration algorithm development.

    Main Results:

    • A proven theorem for efficient local histogram calculation.
    • Successful optical implementation of local histogram computation.
    • Demonstration of rank-order value selection for diverse filtration.

    Conclusions:

    • The proposed theorem provides an effective method for local histogram calculation.
    • Optical implementation offers a high-speed solution for image analysis tasks.
    • The approach facilitates the development of a wide range of nonlinear image filters.