Cluster img and Image File Readers: Difference between pages

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===Purpose===
In addition to support for importing standard image formats (.jpg, .tiff, .gif, .bmp, .png), the following file readers and tools for importing images are also available. See the [[Constructing_Image_DataSets | constructing images page]] for more information about creating image datasets.


Perform automatic clustering of image using sample distances.
==File Readers==
*[[camecaeval]] - Assess information in each mass in a Cameca Ion-Tof file.
*[[camecard]] - Read Cameca Ion-Tof file into MATLAB.
*[[envirdr]] - Import ENVI image files.
*[[imageload]] - Load workspace or standard image files (.jpg, .gif, ect...).
*[[lispixrawreadr]] - Import Lispix Raw formatted image.
*[[opotekenvirdr]] - Reads OPOTEK ENVI image files.
*[[opotektiffrdr]] - Reads OPOTEK multi-layer TIFF files.
*[[rawread]] - Imports Physical Electronics .raw files.


===Synopsis===
==Import Tools==


:[clas,basis,info,h] = cluster_img(x,nclusters,options)
*[[clipboard_image]] - Copy and paste images to/from the system clipboard.  
:clas = cluster_img(x,basis,options)
*[[image_folder_load]] - Load all image files in a selected directory as cell array of image DSOs.  
 
*[[imageload]] - GUI to select, build, then load a image DSO into the DataSet Editor.
===Description===
*[[rawreadgui]] - GUI to import [[rawread|raw]] files.
 
Performs partitional clustering (also known as Divisive Cluster Analysis, or DCA) in which samples are segregated into some pre-defined number of clusters (classes) based on their distance to the means of those classes.
 
The algorithm first identifies the specified number of unique samples (pixels) in a given image and considered the target spectrum for one of the target clusters. All other pixels are then classified into one of those clusters based on which of the target spectra they are closest to. When using the K-means algorithm, the mean of each cluster is then calculated and used as the new target spectrum for that cluster. All the pixels are re-evaluated for their cluster assignments again (allowing for some pixels to change assignment due to the change in target spectra). This process is repeated until pixels stop changing classes or the target spectra stop changing.
 
If the algorithm is DISTSLCT, the means of the classes are not used in place of the target spectra.
 
This function normally also implements a robustness test by discarding any target spectra which accumulate less than a specified percentage of the samples. Thus, very unusual pixels (samples) which are not like a significant portion of the other pixels will not be allowed to create useless clusters with no appreciable membership.
 
====Inputs====
* '''[clas,basis,info,h] = cluster_img(x,nclusters,options)''' - The data to cluster (x), the total number of clusters to form (nclusters), and an optional (options) structure (see below), or
* '''[clas,basis,info,h] = cluster_img(x,max_fract,options)''' - The data to cluster (x), the maximum fraction of samples allowed in a cluster (max_fract), and an optional (options) structure (see below), or
* '''clas = cluster_img(x,basis,options)''' - A "prediction" call given the data to cluster (x), a previously calculated basis set returned by cluster_img (basis), and an optional (options) structure.
 
====Outputs====
Outputs are a vector of integer classes for each sample in x <tt>clas</tt>, the
corresponding normalized basis spectra <tt>basis</tt>, information on the
targets and correlation <tt>info</tt>, and the handle <tt>h</tt> of a figure if
created. The output <tt>info</tt> is a structure containing the following
fields:
* '''corr''' = the correlation of each pixel to each target.
* '''targets''' = the indices of the pixels used as targets.
 
===Options===
 
options =  a structure array with the following fields:
 
* '''plots''': [ 'none' |{'final'}] governs plotting of results.
* '''algorithm''': [ 'distslct' |{'kmeans'}] algorithm for determining classes distslct is based solely on most unique samples; kmeans adjusts class target to mean of class (iterative).
* '''minarea''': { 1 } minimum area (in %) that a class must account for to be retained as a unique class.
* '''preprocessing''': { [] } preprocessing structure (see PREPROCESS).
* '''pca''': [ {'no'} | 'yes' ] When 'yes', A PCA model is calculated from the data and the scores are used to perform clustering. The output basis is in terms of the original variables.
* '''ncomp''': [ 1 ] Number of PCs (components) to retain from the PCA model. Only used if options.pca is 'yes'.
 
===See Also===
 
[[cluster]], [[distslct]], [[knn]]

Revision as of 17:06, 5 October 2012

In addition to support for importing standard image formats (.jpg, .tiff, .gif, .bmp, .png), the following file readers and tools for importing images are also available. See the constructing images page for more information about creating image datasets.

File Readers

  • camecaeval - Assess information in each mass in a Cameca Ion-Tof file.
  • camecard - Read Cameca Ion-Tof file into MATLAB.
  • envirdr - Import ENVI image files.
  • imageload - Load workspace or standard image files (.jpg, .gif, ect...).
  • lispixrawreadr - Import Lispix Raw formatted image.
  • opotekenvirdr - Reads OPOTEK ENVI image files.
  • opotektiffrdr - Reads OPOTEK multi-layer TIFF files.
  • rawread - Imports Physical Electronics .raw files.

Import Tools

  • clipboard_image - Copy and paste images to/from the system clipboard.
  • image_folder_load - Load all image files in a selected directory as cell array of image DSOs.
  • imageload - GUI to select, build, then load a image DSO into the DataSet Editor.
  • rawreadgui - GUI to import raw files.
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