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Related Concept Videos

What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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Conservative Site-specific Recombination and Phase Variation02:53

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Variation of Atmospheric Pressure01:18

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Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Treatment of Ankle Osteoarthritis with Total Ankle Replacement Through a Lateral Transfibular Approach
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A Total Bounded Variation Approach to Low Visibility Estimation on Expressways

Xiaogang Cheng1,2,3, Bin Yang4,5, Guoqing Liu6

  • 1College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; xiacheng@kth.se or chengxg@njupt.edu.cn

Sensors (Basel, Switzerland)
|February 1, 2018
PubMed
Summary

This study introduces a novel computer vision method using total bounded variation (TBV) to accurately estimate low atmospheric visibility, crucial for preventing expressway accidents caused by fog and haze.

Keywords:
total bounded variationimage spectrumlow visibility estimationpiece stationaryfog and haze

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Area of Science:

  • Computer Vision
  • Environmental Monitoring
  • Traffic Safety

Background:

  • Low visibility due to fog and haze on expressways is a significant cause of traffic accidents.
  • Accurate real-time atmospheric visibility estimation is vital for mitigating accident rates.
  • Existing computer vision methods require enhanced accuracy for complex, time-varying fog and haze conditions.

Purpose of the Study:

  • To develop and validate a computer vision approach for estimating low atmospheric visibility (below 300 m).
  • To improve the accuracy of visibility estimation in foggy and hazy conditions.
  • To provide a reliable method for real-time visibility assessment on expressways.

Main Methods:

  • A total bounded variation (TBV) approach combined with image spectrum analysis was developed.
  • Foggy/hazy images were treated as pseudo-blurred images, and clear day images as references.
  • Machine learning and piecewise stationary time series analysis established a relationship between TBV and real visibility.

Main Results:

  • A method was proposed to filter low visibility images using spectrum features (high-frequency coefficient ratio < 20%).
  • A piecewise function was established for visibility estimation based on TBV and machine learning.
  • Validation on real surveillance data from Tongqi expressway showed relative errors less than 10%.

Conclusions:

  • The proposed TBV-based computer vision approach effectively estimates low atmospheric visibility.
  • The method demonstrates high accuracy and reliability, outperforming image contrast models.
  • This technique offers a promising solution for real-time visibility monitoring to enhance expressway safety.