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Shift mean image tracking

wohaofan
2015-09-09 04:00:06
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MatlabMatlab ScalaScala

Description

Application background

  first of all, it is a branch of mathematical statistics, which is a branch of mathematical statistics. The parameter density estimation method requires that the feature space is subject to a known probability density function, and it is very difficult to achieve this condition in the practical application. But no parameter density estimation method requires the least prior knowledge, and it can be used to estimate the training data and can be used to estimate the density of arbitrary shape. Therefore, the density function of the probability density function is not specified in the form of the structure of the probability density function. Commonly used nonparametric density estimation methods are: histogram method, nearest neighbor method and kernel density estimation method.

Key Technology

  MeanShift algorithm is a kernel density estimation method, which does not require any prior knowledge and is completely dependent on the sample points in the feature space. For a group of sampled data, histogram method usually data of the range is divided into several equal interval, data according to the interval is divided into a number of groups, probability of the value of the number and the number of parameters in each group of data rate is each unit; kernel density estimation method similar to the histogram method is a nuclear function for smoothing the data. Using kernel function estimation method, in the case of sampling, it can converge to arbitrary density function gradually, that is, it can be estimated that the data of any distribution can be estimated.
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File list

Tips: You can preview the content of files by clicking file names^_^
Name Size Date
CImg.h1.14 MB08-03-07 15:09
colourModel.cpp6.26 kB21-03-07 20:32
colourModel.h2.31 kB20-03-07 10:17
MsTracker.cpp10.27 kB21-03-07 20:25
MsTracker.h1.47 kB22-03-07 16:10
Rademe.txt662.00 B06-04-08 21:27
<mean>0.00 B28-04-07 20:25
...
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Shift mean image tracking (184.10 kB)

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