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Related Concept Videos

Skewness01:06

Skewness

The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency are...
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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.
The LOD indicates the presence or absence...

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Related Experiment Video

Updated: May 26, 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

Difference-Based Image Noise Modeling Using Skellam Distribution.

Youngbae Hwang1, Jun-Sik Kim, In So Kweon

  • 1Multimedia IP Center, Korea Electronics Technology Institute (KETI), #68 Yatap-dong, Bundang-gu, Seongnam-si, Gyeonggido 463-816, Korea. ybhwang@keti.re.kr

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 7, 2011
PubMed
Summary
This summary is machine-generated.

Pixel intensity is a random variable, not a fixed value. A new Skellam distribution model accurately captures intensity differences, outperforming traditional Gaussian models in image analysis and noise detection.

Related Experiment Videos

Last Updated: May 26, 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
  • Quantum Physics

Background:

  • Pixel intensity is conventionally modeled as an additive Gaussian random variable.
  • Quantum physics suggests pixel intensity is inherently a random variable, challenging traditional models.
  • Existing models may not accurately represent noise distributions under natural illumination.

Purpose of the Study:

  • To propose a novel model for pixel intensity differences.
  • To validate the Skellam distribution as a model for intensity differences derived from Poisson photon noise.
  • To establish a robust method for distinguishing signal from noise in images.

Main Methods:

  • Directly modeling pixel intensity differences using the Skellam distribution.
  • Deriving the Skellam distribution from a Poisson photon noise model.
  • Establishing an intensity-Skellam parameter linearity invariant to scene and camera parameters.
  • Proposing practical methods for obtaining the intensity-Skellam line.

Main Results:

  • The Skellam distribution accurately models intensity differences, outperforming conventional additive Gaussian models.
  • A linear relationship was found between intensity and Skellam parameters under natural illumination.
  • This intensity-Skellam line demonstrated invariance to scene, illumination, and camera parameters.
  • The model effectively differentiates between signal and noise-induced intensity variations.

Conclusions:

  • The Skellam distribution provides a more accurate model for pixel intensity noise than traditional methods.
  • The proposed method enables statistically robust noise determination in images.
  • The model's effectiveness was demonstrated in practical applications like background subtraction and edge detection.