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Mean Absolute Deviation01:13

Mean Absolute Deviation

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...
Central Tendency: Analysis01:10

Central Tendency: Analysis

Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
Cross Product01:25

Cross Product

The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.
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...
NMR Spectroscopy: Chemical Shift Overview01:15

NMR Spectroscopy: Chemical Shift Overview

The position of the absorption signal of a sample is reported relative to the position of the signal of tetramethylsilane (TMS), which is added as an internal reference while recording spectra. The difference between the absorption frequencies of the sample and TMS (in Hz) is divided by the spectrometer operating frequency (in MHz) to obtain a dimensionless quantity called the chemical shift. It is reported on the δ (delta) scale and expressed in parts per million.
For instance, the proton...
Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

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Related Experiment Video

Updated: May 30, 2026

Live Imaging Characterization of Centromere Movements During Male Meiotic Prophase in Arabidopsis thaliana
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Live Imaging Characterization of Centromere Movements During Male Meiotic Prophase in Arabidopsis thaliana

Published on: October 24, 2025

Mean shift trackers with cross-bin metrics.

Ido Leichter1

  • 1MSR Advanced Technology Labs Israel, Microsoft Research, Microsoft Israel R&D Center, Building 23, Matam Park, Haifa 31905, Israel. idol@microsoft.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 17, 2011
PubMed
Summary

New visual trackers using cross-bin metrics offer improved performance over traditional methods. These trackers are simpler and faster, enhancing efficiency in histogram-based distance measurements for applications like visual tracking.

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Cross-bin metrics are superior to bin-by-bin metrics for histogram distance measurement.
  • Existing robust visual trackers utilize the Earth Mover's Distance (EMD), a cross-bin metric, but involve complex computations.

Purpose of the Study:

  • To derive simpler and faster visual trackers based on Mean Shift (MS) iterations that utilize cross-bin metrics.
  • To improve upon the computational complexity and speed of existing EMD-based trackers.

Main Methods:

  • Developed alternative trackers employing cross-bin metrics integrated with Mean Shift (MS) optimization.
  • Simplified the tracking process by avoiding feature density clustering and multidimensional EMD computations.

Main Results:

  • The proposed MS-based trackers are computationally simpler and faster than previous EMD trackers.
  • These new trackers maintain the benefits of cross-bin metrics without the associated computational overhead.

Conclusions:

  • Mean Shift-based iterations combined with cross-bin metrics provide an efficient alternative for visual tracking.
  • The derived trackers offer a practical improvement for applications requiring robust histogram comparisons.