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Related Concept Videos

Probability Histograms01:17

Probability Histograms

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.
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra. Schrödinger...
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...
Energy Diagrams - II01:10

Energy Diagrams - II

Energy diagrams are important to understand the dynamics of a system. The topology of an energy diagram helps illustrate the equilibrium points of the system.
The point in the energy diagram at which the system’s potential energy is the lowest is known as the local minima. The system tends to stay in this position indefinitely unless acted upon by a net force. The slope of the potential energy diagram at the local minima is zero, indicating that zero net force is acting on the system. The slope...
Photoelectric Effect02:26

Photoelectric Effect

When light of a particular wavelength strikes a metal surface, electrons are emitted. This is called the photoelectric effect. The minimum frequency of light that can cause such emission of electrons is called the threshold frequency, which is specific to the metal. Light with a frequency lower than the threshold frequency, even if it is of high intensity, cannot initiate the emission of electrons. However, when the frequency is higher than the threshold value, the number of electrons ejected...

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Related Experiment Video

Updated: May 31, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

An energy-based model for the image edge-histogram specification problem.

Max Mignotte

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 23, 2011
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an energy-based model for edge-histogram specification, enhancing image luminance distribution control. The novel approach iteratively adjusts image edges to match a target edge distribution for improved image processing.

    Related Experiment Videos

    Last Updated: May 31, 2026

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Photography

    Background:

    • Extends Coltuc et al.'s exact specification method for image luminance distribution.
    • Addresses the need for precise control over image edge characteristics.

    Discussion:

    • Presents an energy-based model formulated as an optimization problem.
    • Each edge iteratively approaches specified gradient magnitudes based on a target edge distribution.
    • Employs a hybrid optimization scheme combining conjugate-gradient and Metropolis criterion search.

    Key Insights:

    • Achieves edge-histogram specification for real input images.
    • Enables precise control over image gradient magnitudes and edge distributions.
    • The hybrid optimization scheme ensures reliable solutions for the energy-based model.

    Outlook:

    • Potential for diverse image processing applications.
    • Further research into advanced image manipulation techniques.
    • Integration with other image enhancement and restoration methods.