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Normal distribution properties in computer vision
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Normal distribution properties in computer vision
1.
Computer vision: models, learning
and inference Chapter 5 The normal distribution Please send errata to s.prince@cs.ucl.ac.uk
2.
Univariate Normal Distribution For
short we write: Univariate normal distribution describes single continuous variable. Takes 2 parameters and 2>0 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 2
3.
Multivariate Normal Distribution For
short we write: Multivariate normal distribution describes multiple continuous variables. Takes 2 parameters • a vector containing mean position, • a symmetric “positive definite” covariance matrix Positive definite: is positive for any real Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 3
4.
Types of covariance
Symmetric Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 4
5.
Diagonal Covariance =
Independence Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 5
6.
Decomposition of Covariance Consider
green frame of reference: Relationship between pink and green frames of reference: Substituting in: Full covariance can be decomposed Conclusion: into rotation matrix and diagonal Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 6
7.
Transformation of Variables If
and we transform the variable as The result is also a normal distribution: Can be used to generate data from arbitrary Gaussians from standard one Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 7
8.
Marginal Distributions
Marginal distributions of a multivariate normal are also normal If then Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 8
9.
Conditional Distributions If then
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 9
10.
Conditional Distributions For spherical
/ diagonal case, x1 and x2 are independent so all of the conditional distributions are the same. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 10
11.
Product of two
normals (self-conjugacy w.r.t mean) The product of any two normal distributions in the same variable is proportional to a third normal distribution Amazingly, the constant also has the form of a normal! Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 11
12.
Change of Variables If
the mean of a normal in x is proportional to y then this can be re-expressed as a normal in y that is proportional to x where Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 12
13.
Conclusion • Normal distribution
is used ubiquitously in computer vision • Important properties: • Marginal dist. of normal is normal • Conditional dist. of normal is normal • Product of normals prop. to normal • Normal under linear change of variables Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 13
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