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Related Concept Videos

Standard Deviation01:10

Standard Deviation

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The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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Mean Absolute Deviation01:13

Mean Absolute Deviation

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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Distance Corrections01:15

Distance Corrections

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Power Factor Correction01:20

Power Factor Correction

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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

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Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
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Related Experiment Video

Updated: Jan 21, 2026

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging
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Lighting Deviation Correction for Integrating-Sphere Multispectral Imaging Systems.

Zhe Zou1, Hui-Liang Shen2, Shijian Li3

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)
|August 14, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a two-stage correction method to improve spectral reflectance measurements in multispectral imaging systems. The technique significantly reduces measurement inconsistency, enhancing spectral and colorimetric accuracy for practical applications.

Keywords:
integrating spherelighting deviationmultispectral imagingspectral reflectance

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Area of Science:

  • Optics and Photonics
  • Color Science
  • Image Processing

Background:

  • Integrating sphere multispectral imaging systems suffer from measurement inconsistencies.
  • Multiple light reflections and non-uniform optical components cause variations in spectral reflectance acquisition.
  • Sample-induced lighting changes and spatial dependency of reflectance are key challenges.

Purpose of the Study:

  • To develop and evaluate a novel two-stage correction method for multispectral imaging systems.
  • To address measurement inconsistencies arising from lighting variations and spatial dependencies.
  • To improve the spectral and colorimetric accuracy of reflectance measurements.

Main Methods:

  • A two-stage correction approach was implemented.
  • Stage one utilizes a white board and white patch for non-uniformity and lighting deviation correction, assuming Lambertian reflection.
  • Stage two employs polynomial regression to correct for non-Lambertian sample reflections and lighting inconsistency.

Main Results:

  • The proposed method was evaluated using real multispectral imaging data.
  • Experimental results demonstrate a considerable elimination of measurement inconsistency.
  • Significant improvements in spectral and colorimetric accuracy were observed.

Conclusions:

  • The developed correction method effectively resolves measurement inconsistencies in integrating sphere multispectral imaging.
  • This advancement is crucial for enhancing the reliability of color measurement in practical applications.
  • The method offers a robust solution for accurate spectral reflectance acquisition.