Detrend img and Ewfa img: Difference between pages

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(New page: ===Purpose=== Evolving window factor analysis for images. ===Synopsis=== :eigs = ewfa_img(spec,window,nl,options) ===Description=== EWFA_IMG performs an SVD of the data in each window and ...)
 
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===Purpose===
===Purpose===
Detrends images.
Evolving window factor analysis for images.
===Synopsis===
===Synopsis===
:[dimg, params, fitsurf] = detrend_img(img, order)
:eigs = ewfa_img(spec,window,nl,options)
===Description===
===Description===
Removes a linear or quadratic trend from an image.''' '''Inputs are the image to be detrended, (img), and the order''' '''of the polynomial to use in detrending, (order) which can be''' '''1, 2 or 3. Outputs are the detrended image (dimg), the parameters''' '''used to detrend (params) and the fitted surface (fitsurf)'''.'''
EWFA_IMG performs an SVD of the data in each window and then counts the number of singular values above the noise level (nl). Each element of the output (eigs) is the center of the window and contains the number of singular values above the noise level (nl).
====INPUTS====
* '''spec''' = MxNxP 3-way data matrix/image (spatial modes 1 and 2 MxN, and spectral mode is 3 w/ P channels).
* '''window''' = 2 element vector containing the window width in the x- and y-directions {each element should be > 1} (Note: if a scalar is input then the window in both directions is set to the scalar).
*  '''nl''' = the approximate noise level in the data.
====OUTPUTS====
* '''eigs''' = number of eigenvalues > nl in each window.
===Options===
''options'' is a structure is a structure array with the following fields:
* '''plots''': [ 'none' | {'final'} ] governs level of plotting,
===See Also===
===See Also===
[[autocor_img]], [[fft_texture]], [[semivar]], [[svd_texture]], [[texture]]
[[evolvfa]], [[efa_demo]], [[ewfa]], [[pca]], [[wtfa]], [[wtfa_img ]]

Latest revision as of 10:30, 29 September 2009

Purpose

Evolving window factor analysis for images.

Synopsis

eigs = ewfa_img(spec,window,nl,options)

Description

EWFA_IMG performs an SVD of the data in each window and then counts the number of singular values above the noise level (nl). Each element of the output (eigs) is the center of the window and contains the number of singular values above the noise level (nl).

INPUTS

  • spec = MxNxP 3-way data matrix/image (spatial modes 1 and 2 MxN, and spectral mode is 3 w/ P channels).
  • window = 2 element vector containing the window width in the x- and y-directions {each element should be > 1} (Note: if a scalar is input then the window in both directions is set to the scalar).
  • nl = the approximate noise level in the data.

OUTPUTS

  • eigs = number of eigenvalues > nl in each window.

Options

options is a structure is a structure array with the following fields:

  • plots: [ 'none' | {'final'} ] governs level of plotting,

See Also

evolvfa, efa_demo, ewfa, pca, wtfa, wtfa_img

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