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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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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.
Relative Frequency Histogram01:14

Relative Frequency Histogram

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...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...

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Sum and difference histograms for texture classification.

M Unser1

  • 1Signal Processing Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland; Biomedical Engineering and Instrumentation Branch, National Institutes of Health, Bethes.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

Sum and difference histograms offer an efficient texture analysis alternative to co-occurrence matrices. This method reduces computation time and memory storage for texture classification.

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Area of Science:

  • Image processing and pattern recognition
  • Statistical texture analysis

Background:

  • Co-occurrence matrices are standard for texture analysis but computationally intensive.
  • Texture characterization relies on analyzing spatial relationships of pixel intensities.

Purpose of the Study:

  • To introduce sum and difference histograms as a computationally efficient alternative for texture analysis.
  • To develop texture classifiers based on sum and difference histograms.
  • To compare the performance of sum and difference histograms with co-occurrence matrices.

Main Methods:

  • Decorrelating random variables to define principal axes of joint probability functions.
  • Generating sum and difference histograms from image data.
  • Implementing maximum likelihood texture classifiers using these histograms or associated global measures.

Main Results:

  • Sum and difference histograms effectively capture texture information.
  • Conjoint use of sum and difference histograms achieves performance comparable to co-occurrence matrices for texture discrimination.
  • The proposed method significantly reduces computation time and memory requirements.

Conclusions:

  • Sum and difference histograms provide a viable and efficient alternative to co-occurrence matrices for texture analysis.
  • This approach offers practical advantages in terms of computational cost and storage.
  • The method is effective for texture classification tasks.